Climate change is leading to more extreme weather hazards, forcing human populations to
be displaced. We employ explainable machine learning techniques to model and …

DoubleML is an open-source Python library implementing the double machine learning
framework of Chernozhukov et al.(2018) for a variety of causal models. It contains …

We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages
graphical causal models. Unlike existing causality libraries, which mainly focus on effect …

Accurate estimating causal effects of crashes on highway traffic is crucial for mitigating the
negative impacts of crashes. Previous studies have built up a series of methods via …

$\texttt {gCastle} $ is an end-to-end Python toolbox for causal structure learning. It provides
functionalities of generating data from either simulator or real-world dataset, learning causal …

Background The impact of the COVID-19 vaccination campaign in the US has been
hampered by a substantial geographical heterogeneity of the vaccination coverage. Several …

Abstract Legislators in the European Union have long been concerned with the
environmental impact of farming activities and introduced so-called agri-environment …

Flexible machine learning tools are being used increasingly to estimate heterogeneous
treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal …

The performance of most causal effect estimators relies on accurate predictions of high-
dimensional non-linear functions of the observed data. The remarkable flexibility of modern …

Estimation of causal effects involves crucial assumptions about the data-generating process,
such as directionality of effect, presence of instrumental variables or mediators, and whether …