2022

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."
Dae Woong Ham and Luke Miratrix present "A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step." Wednesday, September 28, 2022
The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption that the treatment group, absent treatment, would change "similarly" to the control group over time. To increase the plausibility of this assumption, a natural idea is to match treated and control units prior to a DiD analysis. In this paper, we characterize the bias of matching under a class of linear structural models with both observed and unobserved confounders that have time varying effects. Given this framework, we find that matching on baseline covariates generally reduces the... Read more about Dae Woong Ham and Luke Miratrix present "A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step."
Matthew Blackwell presents "Difference-in-differences Designs for Controlled Direct Effects" Wednesday, September 21, 2022
Political scientists are increasingly interested in controlled direct effects, which are important quantities of interest for understanding why, how, and when causal effects will occur. Unfortunately, their identification has usually required strong and often unreasonable selection-on-observeables assumptions for the mediator. In this paper, we show how to identify and estimate controlled direct effects under a difference-in-differences design where we have measurements of the outcome and mediator before and after treatment assignment. This design allows us to weaken the identification... Read more about Matthew Blackwell presents "Difference-in-differences Designs for Controlled Direct Effects"
Cory McCartan presents "Individual and Differential Harm in Redistricting" Wednesday, September 14, 2022
Social scientists have developed dozens of measures for assessing partisan bias in redistricting. But these measures cannot be easily adapted to other groups, including those defined by race, class, or geography. Nor are they applicable to single- or no-party contexts such as local redistricting. To overcome these limitations, we propose a unified framework of harm for evaluating the impacts of a districting plan on individual voters and the groups to which they belong. We consider a voter harmed if their chosen candidate is not elected under the current plan, but would be under a different... Read more about Cory McCartan presents "Individual and Differential Harm in Redistricting"
Xiang Zhou presents "Marginal Interventional Effects" Wednesday, September 7, 2022

 Conventional causal estimands, such as the average treatment effect (ATE), reflect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often incremental, changing the treatment status for only a small segment of the population who are at or near “the margin of participation.” To capture this notion, two parallel lines of inquiry have developed in economics and in statistics and epidemiology that define, identify, and estimate what we call interventional effects. In this article,...

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