In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach ena...

In this work, we propose a simple but e‚ective method to interpret black-box machine learning models globally. Œat is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. Œis tree is learned from the contribution matrix which consists of the contributions of input...

In this paper we consider two-dimensional progress graphs and compare the complexity of the reduced state transition systems obtained by two different partitioning heuristics based on dihomotopy. We show experimentally that a recursive reduction algorithm could improve the results of Goubault et al.

In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the compet...