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Recent Developments in the Econometrics of Program Evaluation
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Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoretical literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas of empirical microeconomics. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical researchers, as well as provide a historical overview of the area and give references to more technical research.
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“The live experiment was specifically designed to measure the model's impact on that metric by using a difference-in-differences statistical procedure - see Stock and Watson (2003) for a textbook discussion and see Imbens and Wooldridge (2009) for a survey on the use of difference-in-differences for program evaluation.”
From Clearance Pricing Optimization for a Fast-Fashion Retailer (2012) · cites this paper as Imbens and Wooldridge (2009)
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“Moreover, we match treatment and control groups on a wider range of individual- and school-level characteristics using reweighting on the propensity of treatment (68) and maximum-entropy balancing (69).”
From Learning loss due to school closures during the COVID-19 pandemic (2021) · cites this paper as [68]
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“All these labels referred to some variant of the assumption that adjusting treatment and comparison groups for differences in observed covariates X (i.e., pretreatment variables) removed all biases in comparisons between treated and comparison units (Imbens & Wooldridge, 2009).”
From Interventions to improve the labour market outcomes of youth: A systematic review of training, entrepreneurship promotion, employment services and subsidized employment interventions (2017) · cites this paper as Imbens & Wooldridge, 2009
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“because extension participation is voluntary and discussion groups may attract more motivated farmers with better performances, concerns remain regarding time-varying unobserved heterogeneity (Imbens and Wooldridge, 2009).”
From Extension and management pathways for enhanced farm sustainability: evidence from Irish dairy farms (2023) · cites this paper as Imbens and Wooldridge, 2009
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Labor Supply Response to the Earned Income Tax Credit
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NONPARAMETRIC BOUNDS ON TREATMENT EFFECTS
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Do Voters Affect or Elect Policies? Evidence from the U. S. House
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Matching to remove bias in observational studies
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Efficient Semiparametric Estimation of Quantile Treatment Effects
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The Nonparametric Identification of Treatment Effects in Duration Models
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Estimation and Confidence Regions for Parameter Sets in Econometric Models
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Bayesian inference for causal effects in randomized experiments with noncompliance
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Self-citationAccounting for No-Shows in Experimental Evaluation Designs
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Sensitivity to Exogeneity Assumptions in Program Evaluation
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Formal mode of statistical inference for causal effects
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Covariance Adjustment in Randomized Experiments and Observational Studies
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An Empirical Framework for Testing Theories About Complimentarity in Organizational Design
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Statistical Treatment Rules for Heterogeneous Populations
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Metadata
- Authors
- Imbens, G. W., & Wooldridge, J. M.
- Affiliations
- National Bureau of Economic Research (US)
- Michigan State University (US)
- Venue
- Journal of Economic Literature
- Publisher
- American Economic Association
- Published
- 2009-03-01
- Volume
- 47
- Issue
- 1
- Pages
- 5-86
- ISSN
- 0022-0515 (ISSN-L)
- OpenAlex
- W3121385028
- Cited by
- 4,926
- References
- 374
- Citations by year
- 2026: 313 · 2025: 400 · 2024: 319 · 2023: 303 · 2022: 298 · 2021: 325 · 2020: 351 · 2019: 346 · 2018: 331 · 2017: 289 · 2016: 270 · 2015: 284 · 2014: 273 · 2013: 224 · 2012: 201
- FWCI
- 164.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Economic Literature (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- program evaluation, causal effects, causal inference, policy evaluation, labor economics, public finance, development economics, industrial organization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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