Double Machine Learning for Static Panel Models with Fixed Effects
This work addresses the challenge of causal inference in panel data for researchers and practitioners, offering incremental improvements by adapting existing methods to nonlinear contexts.
The paper tackles the problem of estimating causal effects in panel data with fixed effects by developing novel double machine learning procedures that extend linear estimators to nonlinear models, and it demonstrates their performance through simulations and a re-estimation of the impact of minimum wage on voting behavior in the UK, recommending first-differencing and ensemble learning for optimal accuracy.
Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)'s partially linear regression model with fixed effects and unspecified nonlinear confounding. Our simulation study assesses the performance of these procedures using different machine learning algorithms. We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK. From our results, we recommend the use of first-differencing because it imposes the fewest constraints on the distribution of the fixed effects, and an ensemble learning strategy to ensure optimum estimator accuracy.