Heterogeneous treatment effects are the center of gravity in many modern causal inference applications. In this paper, we investigate the estimation and inference of heterogeneous treatment effects with precision in a general non-parametric setting. To this end, we enhance the classical...

If a probability distribution is sufficiently close to a normal distribution, its density can be approximated by the truncated Gram-Charlier series where skewness and kurtosis directly appear as parameters. However, the existing literature is restricted to truncating the series expansion until...