Before revision:  Further , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio by exploiting an explicit form of the efficient influence function. Using our new estimators , we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example .
After revision:  Further , we propose semparametrically efficient , easy-to-implement, machine-learning-friendly estimators of the aggregated (log) odds ratio . Further , we develop methods for causal inference and illustrate the usefulness of our methods by a real-data example .