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Carlos Velasco Rivera presents "On-Platform Experimental Research on Facebook and Instagram in the 2020 Election" Wednesday, March 22, 2023
We will discuss a groundbreaking collaboration among over two dozen independent (that is, not paid by Meta) academics and a team of Meta researchers. Since early 2020, this group has worked together to evaluate the role of Facebook and Instagram in the 2020 U.S. presidential election. The collaboration has, as of now, resulted in over a dozen pre-registered (observational and experimental) designs for academic research papers. In this presentation, we will focus on the experimental interventions that were designed to test the causal impact of Facebook and Instagram on all of the project’s key... Read more about Carlos Velasco Rivera presents "On-Platform Experimental Research on Facebook and Instagram in the 2020 Election"
Yi Zhang presents "Safe Policy Learning under Regression Discontinuity Designs" Wednesday, February 8, 2023
The regression discontinuity (RD) design is widely used for program evaluation with observational data. The RD design enables the identification of the local average treatment effect (LATE) at the treatment cutoff by exploiting known deterministic treatment assignment mechanisms. The primary focus of the existing literature has been the development of rigorous estimation methods for the LATE. In contrast, we consider policy learning under the RD design. We develop a robust optimization approach to finding an optimal treatment cutoff that improves upon the existing one. Under the RD design,... Read more about Yi Zhang presents "Safe Policy Learning under Regression Discontinuity Designs"
Elliott Ash presents "Televised Debates and Emotional Appeals in Politics: Evidence from C-SPAN" Wednesday, February 1, 2023
We study the effect of televised broadcasts of floor debates on the rhetoric and behavior of U.S. Congress Members, focusing on a measure of emotionality, relative to rationality, constructed using computational linguistics methods. First, we show in a differencesin-differences analysis that the introduction of C-SPAN broadcasts in 1979 increased the use of emotional appeals in the House relative to the Senate, where televised floor debates were not introduced until later. Second, we use exogenous variation in C-SPAN channel positioning as an instrument for C-SPAN viewership by Congressional... Read more about Elliott Ash presents "Televised Debates and Emotional Appeals in Politics: Evidence from C-SPAN"
Justin Grimmer presents "A Statistical Framework to Engage the Problem of Disengaged Survey Respondents" Wednesday, January 25, 2023
Researchers in academia, government, and industry increasingly rely upon cheaper online surveys to measure public opinion. However, with their lower cost, online surveys increase the risk of bias from inattentive or disengaged survey respondents entering the sample – a risk that remains even after survey firms and researchers use well-developed filters and attention checks to exclude these disengaged respondents. In this paper, we introduce a statistical framework for surveys with disengaged respondents and tools to address the bias. First, we develop a partial identification approach that... Read more about Justin Grimmer presents "A Statistical Framework to Engage the Problem of Disengaged Survey Respondents"
Alberto Abadie presents "Synthetic Controls for Experimental Design" Wednesday, November 30, 2022
This article studies experimental design in settings where the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be exposed to the treatment. In such settings, randomization of the treatment may induce large ex-post estimation biases under many or all possible treatment assignments. We propose a variety of synthetic control designs as experimental designs to select treated units in non-randomized experiments with large aggregate units, as well as the untreated units to be used as a control group. Average potential outcomes are... Read more about Alberto Abadie presents "Synthetic Controls for Experimental Design"
Iván Diaz presents "Causal survival analysis under competing risks using longitudinal modified treatment policies" Wednesday, November 16, 2022
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, numerical, or continuous exposures measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to right-censoring and competing risks. We present... Read more about Iván Diaz presents "Causal survival analysis under competing risks using longitudinal modified treatment policies"
Tian Zheng presents "Toward a Taxonomy of Trust for Probabilistic Machine Learning" Wednesday, November 9, 2022
Probabilistic machine learning increasingly informs critical decisions in all sectors. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of available training data, (2) in the translation of abstract goals on the training data to a concrete mathematical problem, (3) in the use of an algorithm to solve the stated mathematical problem, and (4) in the use of a particular code implementation of the chosen algorithm. Our taxonomy highlights steps... Read more about Tian Zheng presents "Toward a Taxonomy of Trust for Probabilistic Machine Learning"
Edward Kennedy presents "Doubly robust capture-recapture methods for estimating population size" Wednesday, November 2, 2022
Estimation of population size using incomplete lists (also called the capture-recapture problem) has a long history across many biological and social sciences. For example, human rights and other groups often construct partial and overlapping lists of victims of armed conflicts, with the hope of using this information to estimate the total number of victims. Earlier statistical methods for this setup either use potentially restrictive parametric assumptions, or else rely on typically suboptimal plug-in-type nonparametric estimators; however, both approaches can lead to substantial bias, the... Read more about Edward Kennedy presents "Doubly robust capture-recapture methods for estimating population size"
Thomas Leavitt presents "Model selection for Decreasing Dependence on Counterfactual, Identification Assumptions in Controlled Pre-Post Designs." Wednesday, October 12, 2022
Researchers often draw causal leverage from measures of outcomes before and after treatment in both a treated group and an untreated, comparison group. Such controlled pre-post designs, e.g., Difference-in-Differences and Comparative-Interrupted-Time-Series, differ in terms of their predictive models and associated counterfactual assumptions to identify the treated group’s average effect (ATT). This paper derives a general, one-stop shop counterfactual assumption — and associated sensitivity analysis — that unifies the differently named assumptions of each design. While the definition of...Read more about Thomas Leavitt presents "Model selection for Decreasing Dependence on Counterfactual, Identification Assumptions in Controlled Pre-Post Designs."
Nima Hejazi presents "Evaluating treatment efficacy in vaccine clinical trials with two-phase designs using stochastic-interventional causal effects." Wednesday, October 5, 2022
In clinical trials randomizing participants to active vs. control conditions and following study units until the occurrence of a primary clinical endpoint, evaluating the efficacy of a quantitative exposure (e.g., drug dosage, drug-induced biomarker activity) is often challenging, as statistical innovations in causal inference have historically focused on estimands compatible only with binary or categorical exposures. Stochastic-interventional effects, which measure the causal effect attributable to perturbing the exposure's natural (i.e., observed) value, provide an interpretable solution.... Read more about Nima Hejazi presents "Evaluating treatment efficacy in vaccine clinical trials with two-phase designs using stochastic-interventional causal effects."

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