Inference on counterfactual distributions

By Victor Chernozhukov, Iv'an Fern'andez-Val, Blaise Melly. 2009

813 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dcfc6750ae9b8354f60bc460f83a030a6bbfd5d5

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Citations in the corpus

The Gender Gap between Earnings Distributions (2019, background): “distribution, and Chernozhukov et al. (2013) discuss the corresponding inferential theory.”
Nonparametric estimation of distributional policy effects (2010, background): “procedures. Chernozhukov, Fernandez-Val, and Melly (2008) derive general limit distribution”
Representation Learning for Treatment Effect Estimation from Observational Data (2018, background): “Estimating the causal effect of an intervention/treatment at individual-level is an important problem that can benefit many domains including health care [12, 1], digital marketing [6, 34, 24], and machine learning [10, 37, 21, 23, 20].”
Partial Distributional Policy Effects (2011, positive): “The latter can be estimated by either the parametric methods discussed in Chernozhukov et al. (2009a), e.g. by first estimating a linear quantile regression model QY |X(τ, x) = x ′β(τ), and then inverting the corresponding conditional quantile function, or by a fully nonparametric CDF…”
Econometric Methods for Program Evaluation (2018, background): “…Dyk, 2004; Cattaneo, 2010; Farrell, 2015; Yang et al., 2016), to quantiles and related treatment effects (Firpo, 2007; Cattaneo, 2010), and to the analysis and interpretation of counterfactual distributional treatment effects (DiNardo et al., 1996; Chernozhukov et al., 2013; Firpo and Pinto, 2016).”
Semiparametric Counterfactual Density Estimation (2021, background): “…a similar goal of moving beyond simple mean summaries to study the entire counterfactual distribution [Abadie, 2002, Chernozhukov and Hansen, 2005, Chernozhukov et al., 2013, Díaz, 2017, Firpo, 2007, Fortin et al., 2011, Frölich and Melly, 2013, Machado and Mata, 2005, Melly, 2005, Rothe, 2010,…”
Universal Off-Policy Evaluation (2021, background): “Outside RL, works in the econometrics and causal inference literature have also considered quantile treatments [29, 99] and inferences on counterfactual distributions [28, 20, 36], but these methods are not developed for sequential decisions and do not provide any high-confidence bounds with guarant”
Statistical Methods for Distributional Analysis (2015, background): “Several empirical studies on income distribution have used kernel density estimation, among other see Marron and Schmitz (1992), Jenkins (1995), Cowell et al. (1996), Daly et al.”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “This idea was extended in a number of recent papers (especially in Chernozhukov, Fernández-Val, and Melly, 2013), and we will exploit this later.”
The changes-in-changes model with covariates (2015, methodology): “The limiting distribution of the plug-in estimator based on (6) can be derived by combining the results in this paper with the results in the Appendix C in Chernozhukov, Fernández-Val, and Melly (2009).”