An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural bre...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the American Statistical Association Vol. 116; no. 536; pp. 1849 - 1864
Main Authors: Chernozhukov, Victor, Wüthrich, Kaspar, Zhu, Yinchu
Format: Journal Article
Language:English
Published: Alexandria Taylor & Francis 02.10.2021
Taylor & Francis Ltd
Subjects:
computer software
Constrained Lasso
Control methods
Counterfactual thinking
Decriminalization
Difference-in-differences
Factor model
Inference
Matrices
Matrix completion
Model-free validity
Panel data
Permutation inference
Permutations
Policy analysis
prediction
Prostitution
Regression analysis
Robustness
Statistical methods
Statistics
Time series
time series analysis
ISSN:0162-1459, 1537-274X, 1537-274X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution. Open-source software for implementing our conformal inference methods is available.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2021.1920957