Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis

TL;DR

This paper introduces a new causal inference method combining counterfactual and synthetic control approaches with instrumented principal component analysis, improving handling of high-dimensional covariates and unobserved confounders.

Contribution

It proposes a novel instrumented PCA approach that enhances causal inference by better modeling factor loadings and covariates, reducing bias and model misspecification.

Findings

Less biased in presence of unobserved covariates

Improved prediction accuracy over existing methods

Effective handling of high-dimensional datasets

Abstract

In this paper, we propose a novel method for causal inference within the framework of counterfactual and synthetic control. Matching forward the generalized synthetic control method, our instrumented principal component analysis method instruments factor loadings with predictive covariates rather than including them as regressors. These instrumented factor loadings exhibit time-varying dynamics, offering a better economic interpretation. Covariates are instrumented through a transformation matrix, $\Gamma$, when we have a large number of covariates it can be easily reduced in accordance with a small number of latent factors helping us to effectively handle high-dimensional datasets and making the model parsimonious. Moreover, the novel way of handling covariates is less exposed to model misspecification and achieved better prediction accuracy. Our simulations show that this method is less biased in the presence of unobserved covariates compared to other mainstream approaches. In the empirical application, we use the proposed method to evaluate the effect of Brexit on foreign direct investment to the UK.