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The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies

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Anna Baiardi, Andrea A. Naghi

General Economics · Submitted 4 Jan 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

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arXiv ID2101.00878 (version 1)
Pith IDTETO6BH4, Pith's permanent record of this paper and its review
CitationAndrea A. Naghi, Anna Baiardi. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies." arXiv:2101.00878 (2021). https://pith.science/paper/TETO6BH4
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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.08 · human participants 0.10 · controlled comparison 0.11 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.63 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; .

A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. background 1.00 0.04 0.03 asserted 1.00
s1 Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. gap 0.98 0.08 0.03 asserted 0.99
s2 We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. contribution 0.99 0.95 0.20 asserted 1.00
s3 We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis. interpretation 0.70 0.87 0.83 asserted 0.90

What kind of paper the classifier reports this is: empirical study 0.91 · evidence from analysis of existing data 0.99.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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  • As pointed out in [ref] , we can group observations according to whether their estimated out-of-bag conditional average treatment effect (CATE) is above or below the median CATE, and we can estimate the average treatment effect separately for these two subgroups.

    Estimating Treatment Effects with Causal Forests: An Application · Application: The Effect of Fox News on the Republican Vote Share · 0.97

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