Debiased machine learning of conditional average treatment effects and other causal functions
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Summary This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularisation bias. We then project the signal onto a set of basis functions, which grow with sample size, to get the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference results automatically target this function. When basis functions are group indicators, the best linear predictor reduces to the group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.
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Semenova and Chernozhukov (2020) provide a method for estimation and inference of the best linear projections of CATEs on observable characteristics.
“Semenova and Chernozhukov (2020) provide a method for estimation and inference of the best linear projections of CATEs on observable characteristics.”
From Prison Rehabilitation Programs: Efficiency and Targeting (2021) · cites this paper as Semenova and Chernozhukov (2020)
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Self-citationKernel Methods for Unobserved Confounding: Negative Controls, Proxies, and InstrumentsPreprint
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Treatment Effect Estimation for User Interest Exploration on Recommender Systems
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Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data
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Propensity Score Regression for Causal Inference With Treatment Heterogeneity
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Call Me Maybe: Does Customer Feedback Seeking Impact Nonsolicited Customers?
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Improved Inference for Doubly Robust Estimators of Heterogeneous Treatment Effects
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Kernel methods for causal functions: dose, heterogeneous and incremental response curves
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Debiased inference on heterogeneous quantile treatment effects with regression rank scores
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Multiply robust estimation of the average treatment effect with missing outcomes
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High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric FrameworkPreprint
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Omitted variable bias in machine learned causal models
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Self-citationCausal Machine Learning for Moderation Effects
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Estimation of partially conditional average treatment effect by double kernel-covariate balancing
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Machine learning for detection of heterogeneous effects of Medicaid coverage on depression
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Do non-farmers pay more for land than farmers?
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Augmented doubly robust post-imputation inference for proteomic data
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Doubly Robust Covariate Shift Regression with Semi-nonparametric Nuisance ModelsPreprint
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Estimating Structural Target Functions using Machine Learning and Influence FunctionsPreprint
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Nonparametric estimation of conditional incremental effects
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Measuring the Efficacy of Amazon’s Recommendation Systems
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Treatment effect heterogeneity and importance measures for multivariate continuous treatments
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Estimating Density Ratio of Marginals to Joint: Applications to Causal Inference
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Observational Price Variation in Scanner Data Does Not Reproduce Experimental Price ElasticitiesPreprint
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Optimal policy learning using Stata
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- Authors
- Semenova, V., & Chernozhukov, V.
- Affiliations
- University of California, Berkeley (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Semenova, Vira
- Chernozhukov, Victor
- ORCID
- Chernozhukov, Victor
- Venue
- The Econometrics Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-08-29
- Volume
- 24
- Issue
- 2
- Pages
- 264-289
- ISSN
- 1368-4221 (ISSN-L)
- 1368-423X
- OpenAlex
- W3048841595
- Cited by
- 167
- References
- 63
- Citations by year
- 2026: 30 · 2025: 40 · 2024: 25 · 2023: 25 · 2022: 25 · 2021: 14 · 2020: 3 · 2019: 4 · 2018: 1
- FWCI
- 11.81
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Econometrics Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- conditional average treatment effect, best linear predictor, Neyman orthogonality, nuisance parameter estimation, simultaneous inference, basis function expansion, confidence bands, price elasticity
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