Online Causal Inference Seminar

Panel discussion: Funding Your Work: Thinking Beyond the NIH

Zijun Gao: Explainability and Analysis of Variance

Wooseok Ha: Semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

Linbo Wang: The synthetic instrument: From sparse association to sparse causation

Jakob Runge: Causal Inference on Time Series Data with the Tigramite Package

Joseph Antonelli: Partial identification & unmeasured confounding with multiple treatment & outcomes

Sizhu Lu: Estimating treatment effects with competing intercurrent events in randomized trials

Jiaqi Zhang: Learning causal cellular programs from large-scale perturbations

Julie Josse: Causal Alternatives to Meta-Analysis

Matias Cattaneo: Estimation and Inference in Boundary Discontinuity Designs

Francesco Locatello: Powering causality with ML: Discovery, Representations, and Inference

Luke Keele: Clustered Observational Studies: A Review of Concepts and Methods

Sam Pimentel: Design Sensitivity and Its Implications for Weighted Observational Studies

Zijian Guo: Multi-Source Learning with Minimax Optimization: Adversarial Robustness to Invariance

Angela Zhou: Robust Fitted-Q-Evaluation & Iteration under Sequentially Exogenous Unobsvd Confounders

Young researchers' seminar: Drago Plečko and Daiqi Gao

[SCI+OCIS] Roundtable Panel – Exploring Career Paths in Pharma, Government, and Technology

Nathan Kallus: Learning Surrogate Indices from Historical A/Bs Adversarial ML for Debiased Inference

Dylan Small: Exploration, Confirmation, and Replication in the Same Observational Study

Guido Imbens: Identification of nonparametric factor models for average treatment effects

Alberto Abadie: Synthetic Controls for Experimental Design

Katherine A. Keith: Proximal Causal Inference with Text Data

[SCI+OCIS] Elizabeth A. Stuart: Lessons in "causality" from National Academies consensus panels

Vasilis Syrgkanis: Detecting clinician implicit biases in diagnoses using proximal causal inference

David Bruns-Smith: Augmented balancing weights as linear regression

Julius von Kügelgen: Multi-Domain Causal Representation Learning

Eric Tchetgen Tchetgen: Revisiting Identification in the Binary IV Model: the NATE and Beyond

Corwin Zigler: Causal health impacts of power plant emission controls ...

Michal Kolesár: Evaluating Counterfactual Policies Using Instruments

Anne Helby Petersen: Two perspectives on interpretable evaluation of causal discovery algorithms