Before revision: This paper studies inferencefor multiple/ many average partial effects when outcome variable is binary or fractional under data rich, cluster sampling environments. The number of average partial effects of interest can be much larger than number of sample clusters. We propose a post-double-selection estimator as well as a Neyman orthogonal moment estimator, both based on \ell_1-penalization, and explore their asymptotic properties.
After revision: This paper studies inferencefor multiple/ many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling environments, we propose a new average partial effect estimator and explore its asymptotic properties.