Double/Debiased Machine Learning for Treatment and Structural Parameters

Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo · The Econometrics Journal / arXiv · 2018 · statistics · 10.1111/ectj.12097 · record chernozhukov2018-double-ml
tag layer · index · concepts · authors · עברית

Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. The single method facet under-describes the paper: it proves general theorems (Section 3, with proofs in the appendix), illustrates them with simulation experiments (Figures 1 and 2), and applies DML to three empirical datasets (Pennsylvania Reemployment Bonus, 401(k) eligibility and participation, Acemoglu-Johnson-Robinson institutions and growth). I set method to mathematical-proof because the theorems are the contribution and the rest is illustration. Quotes were taken only from clean prose: the extraction renders most mathematics as noise ('(cid:98)θ0', 'hk2i'-style artefacts, spaced 'N −1/2'), and the abstract's subscripted parameters appear as plain 'θ0' and 'η0', which is how they are quoted here. Many further explicit self-relations to named prior work exist (Robinson 1988 for the partially linear model, van der Laan and Rubin 2006, Athey et al. 2016 as complementary, Fan et al. 2012 for the term 'cross-fitting'); the three recorded are the ones where the paper states its own position most unambiguously. 2026-08-13 and 2026-08-14 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node. Update, 15 September 2026: EXCEPTION, measured with the merge gate's own predicate (Paper.locate): 1 quote(s) on this record are not found as one exact string in the current staged text - content_tags.statistics[0].evidence. The sentence above does not hold for them as written. Where a row records why - the quote is grounded in another copy of the paper, joins two passages with an ellipsis, or differs from the staged text by extraction noise - that note stays on the row.

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How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Other fields define these differently the same word, defined another way in another discipline
bias 38 fields · machine learning 14 fields · sample-size 5 fields · confidence-interval 2 fields · statistical-significance 2 fields · linear regression 2 fields · and 1 more →

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 10 concept tags on this paper, 6 are grounded in a quoted sentence.

causal inference machine learning treatment effect orthogonalization cross-fitting Neyman orthogonality sample splitting rate requirement identification condition regularization bias A TERM IS BORROWED PARTLY, FOR A PROCEDURE THE SOURCE USED DIFFERENTLY, AND THE INEXACT FIT IS STATED IN THE SAME SENTENCE: we use the term CROSS-FITTING to characterize our recommended procedure, PARTLY BORROWING THE JARGON FROM an earlier paper WHICH EMPLOYED A SLIGHTLY DIFFERENT FORM of sample-splitting. The word's provenance and its imprecision are both on the page A TRADITIONAL ASSUMPTION IS SAID TO BREAK DOWN OUTSIDE ITS REGIME, AND THE REGIME IS NAMED: the paper departs from the classical setting BY ALLOWING the nuisance parameter to be SO HIGH-DIMENSIONAL that the traditional assumptions - SUCH AS DONSKER PROPERTIES, THAT LIMIT COMPLEXITY OF THE PARAMETER SPACE - **BREAK DOWN**. What fails is not a result but a condition, and the condition is named TWO GOOD METHODS COMPOSED NAIVELY ARE SHOWN TO FAIL, AND THE WORD `NAIVELY` IS THE PAPER'S: both regularization bias and overfitting in estimating the nuisance parameter cause **A HEAVY BIAS** in estimators of the target that are obtained BY NAIVELY PLUGGING ML estimators into the estimating equations. Each piece is sound; the composition is not, and the paper names the fault in the composing rather than in either part THE REMEDY IS PRESENTED AS TWO INGREDIENTS AND THE RESULT IS GIVEN TWO NAMES AT ONCE: the impact can be removed using TWO SIMPLE, YET CRITICAL, INGREDIENTS - orthogonal moments with REDUCED SENSITIVITY to nuisance parameters, and cross-fitting - and WE CALL THE RESULTING SET OF METHODS DOUBLE OR DEBIASED ML. One method, two adjectives, offered as alternatives rather than ranked A PROPERTY IS NAMED AFTER THE PERSON WHO INTRODUCED IT, AND THE ATTRIBUTION IS GIVEN AS THE REASON FOR THE NAME: we refer to property (1.8) as NEYMAN ORTHOGONALITY and to the score as the Neyman orthogonal score function DUE TO THE FUNDAMENTAL CONTRIBUTIONS in Neyman (1959) and Neyman (1979), WHERE THIS NOTION WAS INTRODUCED. The name records a debt, not a definition - and the property it names is a numbered equation, so the term is anchored to a formula rather than a gloss THE FORMAL CONDITION IS GIVEN AN INTUITIVE GLOSS THAT STATES WHAT IT BUYS: **INTUITIVELY**, the orthogonality condition means the moment conditions used to identify the parameter are LOCALLY INSENSITIVE to the value of the nuisance parameter, WHICH ALLOWS ONE TO PLUG IN NOISY ESTIMATES of these parameters WITHOUT STRONGLY VIOLATING the moment condition. The gloss is not a restatement - it is the licence the condition grants A FUNCTION IS RENAMED WITH A HEDGING PREFIX WHEN ITS SPECIAL CASE IS GENERALISED: the criterion can be the log-likelihood associated to an observation - MORE GENERALLY, WE REFER TO IT AS THE QUASI-LOG-LIKELIHOOD FUNCTION. The prefix marks that the object no longer has the property its root name asserts A CONDITION IS DECLARED NONPRIMITIVE AND ITS OWN CONDITIONS ARE DECLINED AS CASE-SPECIFIC: THE ONLY NONPRIMITIVE CONDITION HERE is the assumption on the rate of estimating the nuisance parameters - those rates are available for most used methods and are CASE-SPECIFIC, SO WE DO NOT RESTATE the conditions needed to reach them. The assumption is labelled as resting on assumptions that are not given A BOUND THE AUTHORS THEMSELVES IMPOSE IS NAMED THE CRUDE BOUND, AND FINER CONDITIONS ARE OFFERED IN ITS PLACE: a structural feature of the examples allows us to require MUCH FINER CONDITIONS on the quality of the nuisance parameter estimators THAN THE CRUDE BOUND (3.8). The paper's own requirement carries a pejorative as its standing name, and the numbered equation is what the pejorative attaches to A DISPENSED-WITH CONDITION IS REPLACED BY A WEAKER ONE AND THE REPLACEMENT IS THEN CRITICISED IN THE SAME PARAGRAPH: WITHOUT INVOKING DONSKER CONDITIONS, one may still show that the troublesome terms vanish **AS LONG AS** the entropy DOES NOT INCREASE WITH N TOO RAPIDLY - HOWEVER, THESE CONDITIONS ON THE GROWTH OF ENTROPY COULD RESULT IN UNNECESSARILY STRONG RESTRICTIONS ON MODEL COMPLEXITY. What was an assumption about a function class becomes a growth rate; the substitution is announced, and so is the price of the thing substituted in nuisance parameters orthogonal treatment causal bias estimation DML Neyman-orthogonal moments nuisance parameter confidence-interval confidence interval mean mean of median regression linear regression sample-size sample size standard-error standard errors statistical-significance statistically significant variance mathematical-proof

Legend

relation to other work	`claim_relations`	how this paper stands to another: agrees, contradicts, extends, qualifies
claim type	`claim_type`	the shape of the claim: existence, mechanism, method, critique, prediction, null
concept edge	`concept_edges`	an asserted relation between two concepts, with the sentence asserting it
concept	`concepts`	an idea the paper works with, quoted where the paper uses it
content tag	`content_tags`	a finding, definition, quantity, statistic or variable named in the paper
control	`controls`	an experiment run to eliminate a rival explanation
limitation	`limitations`	what the paper says its evidence cannot support
mechanism	`mechanisms`	how the reported effect is said to come about
method	`method`	how the paper produced its evidence
phenomenon	`phenomenon`	the effect or regularity under study
sense	`senses`	a distinct use of a term, quoted where the paper fixes it
transfer	`transfers`	a method or concept carried in from another field
unrecordable relation	`unhoused_relations`	a relation the paper asserts that this vocabulary cannot express, with the reason

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1111/ectj.12097. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 5 · claim_type 1 · concept_edges 1 · concepts 6 · content_tags 13 · controls 1 · limitations 8 · mechanisms 10 · method 1 · phenomenon 1 · senses 3 · transfers 1 · unhoused_relations 1

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 1 relation here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facet	tag	field	conf.	whose words
claim_type	method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_type`		the paper's words
phenomenon	regularization bias in semiparametric inference with machine-learned nuisance parameters Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`phenomenon`		the paper's words
method	mathematical-proof Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mathematical-proof	`method`		the paper's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: A TERM IS BORROWED PARTLY, FOR A PROCEDURE THE SOURCE USED DIFFERENTLY, AND THE INEXACT FIT IS STATED IN THE SAME SENTENCE: we use the term CROSS-FITTING to characterize our recommended procedure, PARTLY BORROWING THE JARGON FROM an earlier paper WHICH EMPLOYED A SLIGHTLY DIFFERENT FORM of sample-splitting. The word's provenance and its imprecision are both on the page	`mechanisms[0]`	0.85	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: A TRADITIONAL ASSUMPTION IS SAID TO BREAK DOWN OUTSIDE ITS REGIME, AND THE REGIME IS NAMED: the paper departs from the classical setting BY ALLOWING the nuisance parameter to be SO HIGH-DIMENSIONAL that the traditional assumptions - SUCH AS DONSKER PROPERTIES, THAT LIMIT COMPLEXITY OF THE PARAMETER SPACE - BREAK DOWN. What fails is not a result but a condition, and the condition is named	`mechanisms[1]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: The sentence that follows the span states the consequence - the naive estimator failing to be N-to-the-minus-one-half consistent - but it is written with inline rate notation that this record's slicing guard refuses, so the clause is described here and not quoted. also in: TWO GOOD METHODS COMPOSED NAIVELY ARE SHOWN TO FAIL, AND THE WORD `NAIVELY` IS THE PAPER'S: both regularization bias and overfitting in estimating the nuisance parameter cause A HEAVY BIAS in estimators of the target that are obtained BY NAIVELY PLUGGING ML estimators into the estimating equations. Each piece is sound; the composition is not, and the paper names the fault in the composing rather than in either part	`mechanisms[2]`	0.85	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: THE REMEDY IS PRESENTED AS TWO INGREDIENTS AND THE RESULT IS GIVEN TWO NAMES AT ONCE: the impact can be removed using TWO SIMPLE, YET CRITICAL, INGREDIENTS - orthogonal moments with REDUCED SENSITIVITY to nuisance parameters, and cross-fitting - and WE CALL THE RESULTING SET OF METHODS DOUBLE OR DEBIASED ML. One method, two adjectives, offered as alternatives rather than ranked	`mechanisms[3]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: A PROPERTY IS NAMED AFTER THE PERSON WHO INTRODUCED IT, AND THE ATTRIBUTION IS GIVEN AS THE REASON FOR THE NAME: we refer to property (1.8) as NEYMAN ORTHOGONALITY and to the score as the Neyman orthogonal score function DUE TO THE FUNDAMENTAL CONTRIBUTIONS in Neyman (1959) and Neyman (1979), WHERE THIS NOTION WAS INTRODUCED. The name records a debt, not a definition - and the property it names is a numbered equation, so the term is anchored to a formula rather than a gloss	`mechanisms[4]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: THE FORMAL CONDITION IS GIVEN AN INTUITIVE GLOSS THAT STATES WHAT IT BUYS: INTUITIVELY, the orthogonality condition means the moment conditions used to identify the parameter are LOCALLY INSENSITIVE to the value of the nuisance parameter, WHICH ALLOWS ONE TO PLUG IN NOISY ESTIMATES of these parameters WITHOUT STRONGLY VIOLATING the moment condition. The gloss is not a restatement - it is the licence the condition grants	`mechanisms[5]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: A FUNCTION IS RENAMED WITH A HEDGING PREFIX WHEN ITS SPECIAL CASE IS GENERALISED: the criterion can be the log-likelihood associated to an observation - MORE GENERALLY, WE REFER TO IT AS THE QUASI-LOG-LIKELIHOOD FUNCTION. The prefix marks that the object no longer has the property its root name asserts	`mechanisms[6]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: The paper makes this disclaimer TWICE, word for word, in Remark 4.1 and Remark 5.1, spelling the term “nonprimitive” in the first and “non-primitive” in the second. The span is the Remark 4.1 instance; the repetition is a property of the file, not of this quote, which is why it is recorded here. also in: A CONDITION IS DECLARED NONPRIMITIVE AND ITS OWN CONDITIONS ARE DECLINED AS CASE-SPECIFIC: THE ONLY NONPRIMITIVE CONDITION HERE is the assumption on the rate of estimating the nuisance parameters - those rates are available for most used methods and are CASE-SPECIFIC, SO WE DO NOT RESTATE the conditions needed to reach them. The assumption is labelled as resting on assumptions that are not given	`mechanisms[7]`	0.95	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: The parenthesis I wanted, “imposes the (crude) rate requirement”, is embedded in display mathematics and cannot be sliced clean. The span is the same paper calling the same bound “the crude bound” in prose, three occurrences later. also in: A BOUND THE AUTHORS THEMSELVES IMPOSE IS NAMED THE CRUDE BOUND, AND FINER CONDITIONS ARE OFFERED IN ITS PLACE: a structural feature of the examples allows us to require MUCH FINER CONDITIONS on the quality of the nuisance parameter estimators THAN THE CRUDE BOUND (3.8). The paper's own requirement carries a pejorative as its standing name, and the numbered equation is what the pejorative attaches to	`mechanisms[8]`	0.85	the tagger's words
mechanisms	mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: A DISPENSED-WITH CONDITION IS REPLACED BY A WEAKER ONE AND THE REPLACEMENT IS THEN CRITICISED IN THE SAME PARAGRAPH: WITHOUT INVOKING DONSKER CONDITIONS, one may still show that the troublesome terms vanish AS LONG AS the entropy DOES NOT INCREASE WITH N TOO RAPIDLY - HOWEVER, THESE CONDITIONS ON THE GROWTH OF ENTROPY COULD RESULT IN UNNECESSARILY STRONG RESTRICTIONS ON MODEL COMPLEXITY. What was an assumption about a function class becomes a growth rate; the substitution is announced, and so is the price of the thing substituted in	`mechanisms[9]`	0.95	the tagger's words
limitations	The regularity conditions given for the treatment-effect results are simplified rather than tightest possible Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the stated regularity conditions: THE CONDITIONS ARE NOT THE TIGHTEST POSSIBLE, BUT OFFER A SET OF SIMPLE CONDITIONS UNDER WHICH THEOREM 5.1 FOLLOWS AS A SPECIAL CASE	`limitations[0]`		the tagger's words
limitations	Assumption 5.1 is simplified for presentation rather than stated at full sharpness Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: Assumption 5.1 as stated: THE CONDITIONS ... ARE FAIRLY SHARP THOUGH SOMEWHAT SIMPLIFIED FOR EASE OF PRESENTATION	`limitations[1]`		the tagger's words
limitations	The empirical finding that more folds gives smaller standard errors does not generalise Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any rule of thumb about cross-fitting folds: THERE DOES NOT APPEAR TO BE A GENERAL RELATIONSHIP BETWEEN THE NUMBER OF FOLDS IN CROSS-FITTING AND THE PRECISION OF THE ESTIMATE OF THE PARAMETER OF INTEREST	`limitations[2]`		the tagger's words
limitations	Deep-learning nuisance estimators were tried but hit stability and computational problems and were left unreported Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the set of nuisance estimators the empirical section reports: deep learning was tried, and WE RAN INTO STABILITY AND COMPUTATIONAL ISSUES AND CHOSE NOT TO REPORT THESE RESULTS IN THE EMPIRICAL SECTION	`limitations[3]`		the tagger's words
limitations	The rate condition the method is known for is a crude bound that follows only under one of the paper's conditions. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the o(N^-1/4) nuisance rate usually quoted from this paper: the authors state it is the crude requirement imposed if only condition (3.7) holds, and that the conditions can be refined	`limitations[4]`	1.0	the tagger's words
limitations	Several of the paper's own headline applications admit weaker conditions than the crude one. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: any application of the crude rate requirement: the authors note that many important structured problems - including partially linear regression models and average treatment effects under unconfoundedness - have vanishing cross-derivatives and so allow more refined requirements	`limitations[5]`	1.0	the tagger's words
limitations	Identification is assumed, not established: it is listed among the conditions the theorems require. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the causal interpretation of estimates from this method: the identification condition is imposed as an assumption alongside Neyman orthogonality and smoothness, so the theory delivers estimation and inference for a parameter that is already identified	`limitations[6]`	1.0	the tagger's words
limitations	The insensitivity to the nuisance estimate is local and approximate, in the authors' own gloss. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the claim that this method is robust to the choice of nuisance estimator: the moment conditions are described as locally insensitive to the nuisance value, permitting noisy plug-in estimates without strongly violating the moment condition	`limitations[7]`	1.0	the tagger's words
transfers	flexible machine-learning estimators used for the nuisance functions of a causal parameter, with Neyman-orthogonal scores and cross-fitting added so their regularization bias does not carry into the estimate Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`transfers[0]`	0.85	the tagger's words
controls	flexibly controlling for a rich set of variables in the 401(k) example, where assignment is observational - and the elimination holds only under the paper's own identifying assumptions, which the sentence states as an aim rather than a demonstrated result Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: bias from the lack of random assignment of the treatment variable · outcome (stated by the tagger, no quotation): partial	`controls[0]`	0.7	the tagger's words
concepts	cross-fitting Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: cross-fitting	`concepts[4]`		the paper's words
concepts	Neyman orthogonality Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Neyman orthogonality	`concepts[5]`		the paper's words
concepts	sample splitting Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: sample splitting	`concepts[6]`		the paper's words
concepts	rate requirement Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: rate requirement	`concepts[7]`		the paper's words
concepts	identification condition Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: identification condition	`concepts[8]`		the paper's words
concepts	regularization bias Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: regularization bias	`concepts[9]`		the paper's words
unhoused_relations	Neyman-orthogonal moments/scores → regularization bias Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. the method REMOVES the impact of a bias that the naive estimator suffers - the paper's own central result, stated as a repair rather than a relation between two objects of study — note: PROVENANCE ROW. This is one of the two instances behind the project's 2026-08-14 ruling: with no type for it, the relation was filed as a 'limitation', where the paper's headline result reads as OUR doubt about the method. It is NO LONGER UNHOUSED - 'causes' with 'effect_direction: eliminates' expresses it, which is what an earlier note of ours added the same evening. Kept so the file records why it exists, and excluded whenever the coverage count is quoted.	`unhoused_relations[0]`		the tagger's words
senses	the o(N^-1/4) rate - A WORST CASE, NOT THE REQUIREMENT: the paper calls it the crude requirement that smooth problems translate to, and says in the same breath that the conditions can be refined, so the number that travels out of this paper is the loosest form of a condition the paper itself tightens Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[0]`	1.0	the tagger's words
senses	Neyman orthogonality - A DERIVATIVE AT A POINT, GLOSSED AS LOCAL INSENSITIVITY: the moment condition is locally insensitive to the nuisance value, which is what licenses plugging in noisy estimates without strongly violating it; the property is evaluated at the truth and both hedges - locally, and without strongly violating - are in the sentence that explains it Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[1]`	1.0	the tagger's words
senses	cross-fitting - COINED IN THIS SENTENCE, WITH ITS DEFINITION ATTACHED: the name is introduced for the procedure of swapping the roles of main and auxiliary samples and averaging the resulting estimates, so unlike the eponyms in our absent-names table this word cannot come apart from its referent - the paper that named it also defined it Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`senses[2]`	1.0	the tagger's words
claim_relations	builds-on → Neyman orthogonality (statistics) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[0]`		the tagger's words
claim_relations	contradicts → the naive plug-in of ML estimators into estimating equations Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[1]`		the tagger's words
claim_relations	builds-on → Neyman (1959) and Neyman (1979) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[2]`		the tagger's words
claim_relations	builds-on → Belloni et al. (2010) and Belloni et al. (2012) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[3]`		the tagger's words
claim_relations	extends → the classical literature on semiparametric estimation Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`claim_relations[4]`		the tagger's words
concept_edges	cross-fitting —is-a→ sample splitting Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. strength of the relation: 1 also in: cross-fitting · sample splitting	`concept_edges[0]`		the tagger's words
content_tags	Double/debiased ML estimates the target parameter with Neyman-orthogonal scores that have reduced sensitivity to nuisance parameters, and with cross-fitting, an efficient form of data-splitting. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.findings[0]`		the tagger's words
content_tags	Naively plugging ML estimates of the nuisance parameters into estimating equations causes a heavy bias in the target estimator, from both regularization bias and overfitting. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above.	`content_tags.findings[1]`		the tagger's words
content_tags	nuisance parameter Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: nuisance parameter	`content_tags.variables[0]`		the tagger's words
content_tags	DML Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: DML	`content_tags.definitions[0]`		the tagger's words
content_tags	Neyman-orthogonal moments Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: Neyman-orthogonal moments	`content_tags.definitions[1]`		the tagger's words
content_tags	cross-fitting Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: cross-fitting	`content_tags.definitions[2]`		the tagger's words
content_tags	mean Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mean	`content_tags.statistics[2]`		the tagger's words
content_tags	median Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE RECOVERED FROM THE PAPER'S OWN CHARACTERS. The recorded quote was LETTERS-ONLY IDENTICAL to this stretch of the staged text but not an exact substring of it - spaces, hyphens, ligature glyphs or mathematical operators differed, so it could never anchor. This is the file's own text between the same two letters: not produced by a rule about the damage, copied out of the file. Round-trip checked (the recovered span is letters-only equal to the original) and length-checked. The text as originally recorded is kept in evidence as extracted. also in: median	`content_tags.statistics[3]`		the tagger's words
content_tags	regression Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: regression	`content_tags.statistics[4]`		the tagger's words
content_tags	sample-size Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: sample-size	`content_tags.statistics[5]`		the tagger's words
content_tags	standard-error Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: standard-error	`content_tags.statistics[6]`		the tagger's words
content_tags	statistical-significance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted. also in: statistical-significance	`content_tags.statistics[7]`		the tagger's words
content_tags	variance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. about this quote: DAMAGED GLYPHS IN THIS QUOTE: (cid:98). These are raw PDF font glyph indices that leaked out in place of the characters they stand for - usually a mathematical operator. The quote is verbatim to the staged text and its anchor is sound; the STAGED TEXT is what is damaged. NOT repaired: the cid-to-character mapping is defined by the font embedded in that particular PDF, so guessing it would produce a clean and unverifiable quote. Repair needs the source PDF and is a re-staging job. also in: variance	`content_tags.statistics[8]`		the tagger's words

Interpretive choices

Tags that could not be placed in the text:

content_tags.statistics[0] — quotation withheld — the page has already reproduced as much of this paper as the rights budget allows
about this quote: DAMAGED GLYPHS IN THIS QUOTE: (cid:104), (cid:105), (cid:112), (cid:12), (cid:98). These are raw PDF font glyph indices that leaked out in place of the characters they stand for - usually a mathematical operator. The quote is verbatim to the staged text and its anchor is sound; the STAGED TEXT is what is damaged. NOT repaired: the cid-to-character mapping is defined by the font embedded in that particular PDF, so guessing it would produce a clean and unverifiable quote. Repair needs the source PDF and is a re-staging job. · NOT MACHINE-VOUCHED: quote-differs-from-our-copy - The quote IS in our copy of this paper - the passage is there, in one place - and is not an exact substring of it. THE DIFFERENCE IS IN THE QUOTE, not in the body: the quote has 'l' where our copy has 'ℓ'; the quote has '̃' where our copy has '˜'; the quote has 'l' where our copy has 'ℓ' (6 differences in all). This body carries 445 '(cid:NNN)' glyph ids, 2 detached diacritics. Nothing is repaired in place: editing either side moves every offset after it and re-hashes every anchor on the record.

What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.