Double Machine Learning for Static Panel Models with Fixed Effects
CoRR(2023)
摘要
Machine Learning (ML) algorithms are powerful data-driven tools for
approximating high-dimensional or non-linear nuisance functions which are
useful in practice because the true functional form of the predictors is
ex-ante unknown. In this paper, we develop estimators of policy interventions
from panel data which allow for non-linear effects of the confounding
regressors, and investigate the performance of these estimators using three
well-known ML algorithms, specifically, LASSO, classification and regression
trees, and random forests. We use Double Machine Learning (DML) (Chernozhukov
et al., 2018) for the estimation of causal effects of homogeneous treatments
with unobserved individual heterogeneity (fixed effects) and no unobserved
confounding by extending Robinson (1988)'s partially linear regression model.
We develop three alternative approaches for handling unobserved individual
heterogeneity based on extending the within-group estimator, first-difference
estimator, and correlated random effect estimator (Mundlak, 1978) for
non-linear models. Using Monte Carlo simulations, we find that conventional
least squares estimators can perform well even if the data generating process
is non-linear, but there are substantial performance gains in terms of bias
reduction under a process where the true effect of the regressors is non-linear
and discontinuous. However, for the same scenarios, we also find -- despite
extensive hyperparameter tuning -- inference to be problematic for both
tree-based learners because these lead to highly non-normal estimator
distributions and the estimator variance being severely under-estimated. This
contradicts the performance of trees in other circumstances and requires
further investigation. Finally, we provide an illustrative example of DML for
observational panel data showing the impact of the introduction of the national
minimum wage in the UK.
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