Machine Learning and Causal Inference for the estimation of the effect of tacrolimus on kidney rejections

Tacrolimus is an immunosuppressive drug given to kidney transplant patients. A low concentration of this drug can lead to kidney rejection, but to our knowledge no research has been done to causally connect the two. This paper investigates the causal effect of tacrolimus concentration on kidney rejection occurrence using predictive analysis and...

Effectiveness of propensity score methods with density estimation in identifying overlap for causal inference

For causal inference, sufficient overlap is needed. It is possible to use propensity scores with the positivity assumption to ensure overlap is present. However, positivity is not enough to properly identify the region of overlap. For this, propensity scores need to be used in combination with density estimation. This project aims to evaluate...

Empirical study of GANITE’s robustness to hidden confounders

An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of...

An empirical study of the effects of unconfoundedness on the performance of Propensity Score Matching

The purpose of this research is to analyze the performance of Propensity Score Matching, a causal inference method for causal effect estimation. More specifically, investigate how Propensity Score Matching reacts to breaking the unconfoundedness assumption, one of its core conceptual pillars. This has been achieved by running PSM on synthetic...