When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

Program evaluation methods are widely applied in economics to assess the effects of policy
interventions and other treatments of interest. In this article, we describe the main …

We investigated the effects of Facebook's and Instagram's feed algorithms during the 2020
US election. We assigned a sample of consenting users to reverse-chronologically-ordered …

We studied the effects of exposure to reshared content on Facebook during the 2020 US
election by assigning a random set of consenting, US-based users to feeds that did not …

Importance Many medical journals, includingJAMA, restrict the use of causal language to the
reporting of randomized clinical trials. Although well-conducted randomized clinical trials …

Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …

Heterogeneous effect estimation is crucial in causal inference, with applications across
medicine and social science. Many methods for estimating conditional average treatment …

Supplement to “Automated versus Do-It-Yourself Methods for Causal Inference: Lessons
Learned from a Data Analysis Competition”. The online supplement contains the full set of …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …