  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 use double machine learning (DML) (Chernozhukov et al., 2018) to
approximate high-dimensional and non-linear nuisance functions of the
confounders to make inferences about the effects of policy interventions from
panel data. We propose new estimators by adapting correlated random effects,
within-group and first-difference estimation for linear models to an extension
of Robinson (1988)'s partially linear regression model to static panel data
models with individual fixed effects and unspecified non-linear confounder
effects. Using Monte Carlo simulations, we compare the relative performance of
different machine learning algorithms and find that conventional least squares
estimators performs well when the data generating process is mildly non-linear
and smooth, but there are substantial performance gains with DML in terms of
bias reduction when the true effect of the regressors is non-linear and
discontinuous. However, inference based on individual learners can lead to
badly biased inference. Finally, we provide an illustrative example of DML for
observational panel data showing the impact of the introduction of the minimum
wage on voting behavior in the UK.
