This paper proposes tools for robust inference for difference-in-differences and eventstudy
designs. Instead of requiring that the parallel trends assumption holds exactly, we impose …

Many economic and causal parameters depend on nonparametric or high dimensional first
steps. We give a general construction of locally robust/orthogonal moment functions for …

We propose a framework for analyzing the sensitivity of counterfactuals to parametric
assumptions about the distribution of latent variables in structural models. In particular, we …

We consider inference in models defined by approximate moment conditions. We show that
near‐optimal confidence intervals (CIs) can be formed by taking a generalized method of …

We propose a way to formalize the relationship between descriptive analysis and structural
estimation. A researcher reports an estimate ĉ of a structural quantity of interest c that is …

What should researchers do when their baseline model is falsified? We recommend
reporting the set of parameters that are consistent with minimally nonfalsified models. We …

Statistical decision rules map data into actions. Point estimators, inference procedures, and
forecasting methods can be viewed as statistical decision rules. However, other types of …

This paper develops a design-based theory of uncertainty that is suitable for analyzing quasi-
experimental settings, such as difference-in-differences (DiD). A key feature of our …

What are “deep uncertainties” and how should their presence influence prudent decisions?
To address these questions, we bring ideas from robust control theory into statistical …

We propose a generalized aggregation approach for model averaging. The entropy-based
optimal criterion is a natural choice for aggregating information from many “globally” …