Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data

48 Pages Posted: 18 Aug 2018

Date Written: August 5, 2018

Abstract

We introduce trajectory balancing, a general reweighting approach to causal inference with time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is exposed to treatment at a given time, while a set of control units remain untreated throughout a time window of interest. First, we show that many commonly used TSCS methods imply an assumption that a unit's non-treatment potential outcomes in the post-treatment period are linear in that unit's pre-treatment outcomes as well as time-invariant covariates. Under this assumption, we introduce the mean balancing method that reweights the control units such that the averages of the pre-treatment outcomes and covariates are approximately equal between the treatment and (reweighted) control groups. Second, we relax the linearity assumption and propose the kernel balancing method that seeks an approximate balance on a kernel-based feature expansion of the pre-treatment outcomes and covariates. The resulting approach inherits the property of handling time-vary confounders as in synthetic control and latent factor models, but has the advantages of: (1) improving feasibility and stability with reduced user discretion compared to existing approaches; (2) accommodating both short and long pre-treatment time periods with many or few treated units; and (3) achieving balance on the high-order "trajectory" of pre-treatment outcomes rather than their simple average at each time period. We illustrate this method with simulations and two empirical examples.

Keywords: causal inference, time-series cross-sectional data, panel data, difference-in-differences, synthetic control, interactive fixed effects, kernel balancing, reweighting

JEL Classification: C31, C33

Suggested Citation

Hazlett, Chad and Xu, Yiqing, Trajectory Balancing: A General Reweighting Approach to Causal Inference With Time-Series Cross-Sectional Data (August 5, 2018). Available at SSRN: https://ssrn.com/abstract=3214231 or http://dx.doi.org/10.2139/ssrn.3214231

Chad Hazlett

UCLA ( email )

405 Hilgard Ave.
Los Angeles, CA 90095-1472
United States

Yiqing Xu (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

HOME PAGE: http://yiqingxu.org

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