This paper reviews, applies, and extends recently proposed methods based on double
machine learning (DML) with a focus on programme evaluation under unconfoundedness …

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 …

This paper considers recent methodological developments in the treatment effects literature,
describes their value for applied evaluation work, and suggests next steps. It pays particular …

We assess the demand effects of discounts on train tickets issued by the Swiss Federal
Railways, the so-called 'supersaver tickets', based on machine learning, a subfield of …

In this article, we study nonparametric inference for a covariate-adjusted regression function.
This parameter captures the average association between a continuous exposure and an …

Uncovering causal effects at various levels of granularity provides substantial value to
decision makers. Comprehensive machine learning approaches to causal effect estimation …

Double machine learning (DML) has become an increasingly popular tool for automated
variable selection in high-dimensional settings. Even though the ability to deal with a large …

We examine the relationship between the usage of household clean cooking fuels in rural
areas and elderly's overall well-being using micro survey data from the China Health and …

Depression has been identified as a risk factor for suicide, yet limited evidence has
elucidated the underlying pathways linking depression to subsequent suicide risk …

Residential self-selection (RSS) confounds the connection between the built environment
and travel behavior. Existing studies have used endogenous switching regression models to …