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Cited by 57Advanced Causal Inference TechniquesEvaluation and Performance AssessmentStatistical Methods and Inference

Machine Learning in Policy Evaluation: New Tools for Causal Inference

Kreif, Noémi, DiazOrdaz, Karla·2019·Oxford Research Encyclopedia of Economics and Finance
DOI 10.1093/acrefore/9780190625979.013.256
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Abstract

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions). This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates. Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.

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  • ML can play a role in causal inference, including in policy problems and in climate-relevant scenarios such as estimating the effects of temperature on human mortality and the effects of World Bank projects on vegetative cover.

    “ML can play a role in causal inference [41,340,619], including in the context of policy problems [40,453] and in climate-relevant scenarios such as estimating the effects of temperature on human mortality [356] and the effects of World Bank projects on vegetative cover [883].”

    From Tackling climate change with machine learning (2023) · cites this paper as [453]

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Authors
Kreif, N., & DiazOrdaz, K.
Affiliations
  • University of York (GB)
  • Department of Medical Statistics, London School of Hygiene and Tropical Medicine
ORCID
Kreif, Noémi
Venue
Oxford Research Encyclopedia of Economics and Finance
Publisher
Oxford University PressNew York, NY
Published
2019-07-29
DOI
10.1093/acrefore/9780190625979.013.256
OpenAlex
W2920490282
Cited by
57
References
120
Citations by year
2026: 2 · 2025: 3 · 2024: 8 · 2023: 20 · 2022: 8 · 2021: 10 · 2020: 5 · 2019: 1
Type
reference entry
Language
en
Open access
closed
Locations
Oxford Research Encyclopedia of Economics and Finance (publishedVersion)
Funders
Medical Research Council (MR/L012332/2, MR/L012332/1)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Keywords
machine learning, policy evaluation, causal inference, average treatment effect, super learner, double machine learning, targeted maximum likelihood estimation, propensity score, feature selection, high-dimensional covariates, model misspecification, semiparametric estimators, LASSO, ensembles

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