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Confidence Sets for Causal Orderings

Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. The primary challenge in causal discovery […]

Life After Bootstrap: Residual Randomization Inference in Regression Models

Join the Salem Center and Panos Toulis (Chicago Booth). Standard statistical inference in regression models, including bootstrap-based procedures, relies on assumptions on the asymptotics of the covariate/error distribution. These assumptions are generally strong—for example, they are typically violated by simple heavy-tailed distributions. In this talk, we propose a new paradigm of inference using randomization theory. Our main […]

Beyond Exclusion: The role of the causal effect of testing on attendance on the day of the test

A Causal Inference Seminar with Magdalena Bennett. High-stake testing plays a crucial role in many educational systems, guiding policies of accountability, resource allocation, and even school choice. However, non-representative patterns of attendance can skew how useful these measures are for accomplishing their main objective. Are we really measuring the quality or performance of a school […]

A graph-theoretic approach for testing causal effects under interference.

David Puelz from UT Austin and the Salem Center presents an approach for randomization tests of causal effects under general forms of interference.  The key idea is to represent a null hypothesis of spillovers as a bipartite graph and condition the test on a biclique in this graph. The approach is completely algorithmic and is […]

Bayesian Models of Treatment Effects: Model Parameterization, Prior Choice, and Posterior Summarization

A Causal Inference Seminar with Jared Murray (UT Austin). Bayesian models are a popular and effective tool for inferring the (possibly heterogeneous) effects of interventions. I will discuss how to carefully specify models and prior distributions to apply judicious regularization of heterogeneous effects. I will also discuss how to extract answers to scientific and policy […]

A Bayesian Semiparametric Approach to Treatment Effect Variation with Noncompliance

Jared Fisher (BYU) joins us to discuss estimating varying treatment effects in randomized trials with noncompliance is inherently challenging since variation comes from two separate sources: variation in the impact itself and variation in the compliance rate. In this setting, existing Frequentist and ML-based methods are quite flexible but are highly sensitive to the so-called […]

Experimental Design for Studying Political Polarization

A Causal Inference Seminar with Alex Volfovsky (Duke). Join us in person at RRH 4.408 or via Zoom here. Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. In our first field experiment Democrats and Republicans followed […]