4 citations · 4 across the 3 of their papers we have counts for
6 papers
What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms
Mikko Väänänen, Fanyu Cui, Carlos Cinelli +1
Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which c…
Pragmatic DML with AI-Learned Representations
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli +5
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is val…
Omitted Variable Bias in Difference-in-Differences Designs
Juejue Wang, Pedro H. C. Sant'Anna, Victor Chernozhukov +1
We study the omitted variable bias (OVB) problem in canonical difference-in-differences (DiD) designs when unobserved confounding induces departures from the parallel trends assump…
Causally Sound Priors for Binary Experiments
Nicholas J. Irons, Carlos Cinelli
We introduce the BREASE framework for the Bayesian analysis of randomized controlled trials with a binary treatment and a binary outcome. Approaching the problem from a causal infe…
Orthogonal Statistical Learning with Self-Concordant Loss
Lang Liu, Carlos Cinelli, Zaid Harchaoui
Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We esta…
Long Story Short: Omitted Variable Bias in Causal Machine Learning
Victor Chernozhukov, Carlos Cinelli, Whitney Newey +2
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment…