Econometric Methods for Program Evaluation

By Alberto Abadie, M. D. Cattaneo. 2018

386 citations recorded by the source corpus.

Program evaluation methods are widely applied in economics to assess the effects of policy interventions and other treatments of interest. In this article, we describe the main methodological frameworks of the econometrics of program evaluation. In the process, we delineate some of the directions along which this literature is expanding, discuss recent developments, and highlight specific areas where new research may be particularly fruitful.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, background): “In many, arguably most, cases, researchers are interested in average treatment effects or other causal or structural parameters (see Abadie and Cattaneo [2018] and Imbens”
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “(2017); Abadie and Cattaneo (2018). We consider the standard setup for program evaluation with observational data: we observe a sample of n units, each exposed to a binary treatment, and for each unit we observe a vector of pre-treatment covariates, X ∈ Rd, treatment status T ∈ {0, 1}, and a scalar ”
Simple Local Polynomial Density Estimators (2018, background): “See Imbens and Rubin (2015) and Abadie and Cattaneo (2018) for reviews and further references.”
Two-Step Estimation and Inference with Possibly Many Included Covariates (2018, result): “ar panel xed eects literature. Applications. From a practical perspective, our results oer new inference results for many popular estimators in program evaluation and treatment eect settings (e.g.,Abadie and Cattaneo, 2018, and references therein), as well as other areas in empirical microeconomics ”
Regression Discontinuity Designs (2021, neutral): “In the second part, Section 3, we focus on estimation and inference methods for RD designs, presenting the two most common approaches: local polynomial regression methods (Fan and Gijbels, 1996), which are naturally motivated by the continuity framework, and analysis of experiments methods (Rosenbau”
Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs (2018, background): “(For general background on these settings, see Imbens and Rubin (2015) and Abadie and Cattaneo (2018), and references therein.”
Robust Inference Using Inverse Probability Weighting (2018, background): “This paper relates to a large body of literature on program evaluation and causal inference (Imbens and Rubin, 2015; Abadie and Cattaneo, 2018; Hernán and Robins, 2018).”
Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights (2018, background): “In 10For recent reviews, see Imbens and Wooldridge (2009), Wooldridge (2010), and Abadie and Cattaneo (2018).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “…include novel approaches to causal inference (e.g., rapidly evolving quasi- experimental techniques that use the potential outcomes [PO] framework; (Abadie & Cattaneo, 2018; Imbens & Wooldridge, 2009; Imbens & Rubin, 2015) and causal graphs/directed acyclic graphs [DAGs]; (Pearl, 1995; 2009; 2016;…”
A Practical Introduction to Regression Discontinuity Designs (2019, background): “For a review on causal inference and program evaluation methods see Abadie and Cattaneo (2018) and references therein. The RD design was originally proposed by Thistlethwaite and Campbell (1960), and historical as well as early review articles are given by Cook (2008), Imbens and Lemieux (2008), and”