IRTG1792DP2019 028
Group Average Treatment Effects for Observational Studies
Daniel Jacob
Stefan Lessmann
Wolfgang Karl Härdle
Abstract:
The paper proposes an estimator to make inference on key features of
heterogeneous treatment effects sorted by impact groups (GATES) for non-
randomised experiments. Observational studies are standard in policy evaluation
from labour markets, educational surveys and other empirical studies. To control
for a potential selection-bias we implement a doubly-robust estimator in the
first stage. Keeping the flexibility to use any machine learning method to learn
the conditional mean functions as well as the propensity score we also use
machine learning methods to learn a function for the conditional average
treatment effect. The group average treatment effect is then estimated via a
parametric linear model to provide p-values and confidence intervals. The result
is a best linear predictor for effect heterogeneity based on impact groups.
Cross-splitting and averaging for each observation is a further extension to
avoid biases introduced through sample splitting. The advantage of the proposed
method is a robust estimation of heterogeneous group treatment effects under
mild assumptions, which is comparable with other models and thus keeps its
flexibility in the choice of machine learning methods. At the same time, its
ability to deliver interpretable results is ensured.
Keywords:
C01, C14, C31, C63
JEL Classification:
causal inference, machine learning, simulation study, confidence intervals,
multiple splitting, sorted group ATE (GATES), doubly-robust estimator