This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in … treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units …

of the associated IV results. We discuss potential adjustments to IV estimates in the presence of this bias …

treatments are conditionally randomly assigned and the controls are sufficiently flexible to avoid omitted variables bias … treatments. Thus, recent concerns about heterogeneity-induced bias in regressions leveraging potential outcome restrictions (e … contamination bias and propose a new class of efficient estimators of weighted average effects that avoid bias. In a re-analysis of …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … how the bound on the bias depends only on the additional variation that the latent variables create both in the outcome … variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias …