Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

In this review, we discuss three major contributions economists have made to our
understanding of the relationship between the environment and individual well-being. First …

Learning individual-level causal effects from observational data, such as inferring the most
effective medication for a specific patient, is a problem of growing importance for policy …

Estimating individualized treatment effects (ITE) is a challenging task due to the need for an
individual's potential outcomes to be learned from biased data and without having access to …

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …

We examine the impact of a positive and policy-driven change in economic resources
available in utero and during childhood. We focus on the introduction of the Food Stamp …

Boys born to disadvantaged families have higher rates of disciplinary problems, lower
achievement scores, and fewer high school completions than girls from comparable …

Causal inference is essential for data-driven decision making across domains such as
business engagement, medical treatment and policy making. However, research on causal …

This paper investigates the effect of low emission zones on air quality and birth outcomes in
Germany. The staggered introduction of the policy measure creates a credible natural …

This chapter seeks to set out what economists have learned about the effects of early
childhood influences on later life outcomes, and about ameliorating the effects of negative …