Chad Hazlett

Chad Hazlett
University of California, Los Angeles | UCLA · Department of Statistics

About

49
Publications
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Introduction
I am a social science methodologist. My primary interest centers on the challenges of "feasible causal inference": what more can be done to enable practitioners to characterize the causal information in their data, without relying on untenable assumptions? To this end I am particularly interested in both improved sensitivity analysis, and in new identification opportunities. I also apply these tools in substantive research, often on questions of violence and civil conflict.

Publications

Publications (49)
Article
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The reintegration of former members of violent extremist groups is a pressing policy challenge. Governments and policymakers often have to change minds among reticent populations and shift perceived community norms in order to pave the way for peaceful reintegration. How can they do so on a mass scale? Previous research shows that messages from tru...
Preprint
Infant mortality remains high and uneven in much of sub-Saharan Africa. Given finite resources, reducing premature mortality requires effective tools to identifying left- behind populations at greatest risk. While countries routinely use income- or poverty- based thresholds to target policies, we examine whether models that consider other factors c...
Preprint
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With the precipitous decline in response rates, researchers and pollsters have been left with highly non-representative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables, X must be adjusted for, they rarely def...
Article
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Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9 and August...
Article
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Economics games such as the Dictator and Public Goods Games have been widely used to measure ethnic bias in political science and economics. Yet these tools may fail to measure bias as intended because they are vulnerable to self-presentational concerns and/or fail to capture bias rooted in more automatic associative and affective reactions. We exa...
Article
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When working with grouped data, investigators may choose between “fixed effects” models (FE) with specialized (e.g., cluster-robust) standard errors, or “multilevel models” (MLMs) employing “random effects.” We review the claims given in published works regarding this choice, then clarify how these approaches work and compare by showing that: (i) r...
Preprint
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Objectives To investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials. Design Phenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects...
Article
The stability-controlled quasi-experiment (SCQE) is an approach to study the effects of nonrandomized, newly adopted treatments. While covariate adjustment techniques rely on a "no unobserved confounding" assumption, SCQE imposes an assumption on the change in the average nontreatment outcome between successive cohorts (the "baseline trend"). We pr...
Article
One political barrier to climate reforms is the temporal mismatch between short-term policy costs and long-term policy benefits. Will public support for climate reforms increase as climate-related disasters make the short-term costs of inaction more salient? Leveraging variation in the timing of Californian wildfires, we evaluate how exposure to a...
Preprint
Full-text available
With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-...
Article
Understanding how the most severe mass atrocities have historically come to an end may aid in designing policy interventions to more rapidly terminate future episodes. To facilitate research in this area, we construct a new dataset covering all 43 very large mass atrocities perpetrated by governments or non-state actors since 1945 with at least 50,...
Preprint
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This paper introduces the package sensemakr for R and Stata, which implements a suite of sensitivity analysis tools for regression models developed in Cinelli and Hazlett (2020a). Given a regression model, sensemakr can compute sensitivity statistics for routine reporting, such as the robustness value, which describes the minimum strength that unob...
Presentation
Full-text available
Experimental treatments for COVID-19 are increasingly being attempted. The ability to learn about their effects from this usage would be a helpful and more immediate complement to randomized trials, without requiring that we deny access to anybody. This presentation describes a method for doing so, both to maximize what we learn from ongoing treatm...
Preprint
Full-text available
When estimating the effect of a covariate in grouped data, investigators may choose "fixed effects" (FE) approaches with specialized standard errors (e.g., cluster-robust standard errors, CRSE), or multilevel models employing "random effects" (MLM/RE). From over 100 articles in political science, sociology, and education, we find that MLM/RE is oft...
Article
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Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is...
Preprint
Full-text available
What causes some civilians to support peace while others do not after violent conflict? The 2016 referendum for a peace agreement with the FARC in Colombia has spawned a growing literature studying determinants of support for peace, focusing largely on the effects of (i) prior exposure to violence and (ii) political affiliation with the deal's cham...
Article
Does exposure to violence motivate individuals to support further violence or to seek peace? Such questions are central to our understanding of how conflicts evolve, terminate, and recur. Yet, convincing empirical evidence as to which response dominates—even in a specific case—has been elusive, owing to the inability to rule out confounding biases....
Preprint
Full-text available
The stability-controlled quasi-experiment (SCQE) is an approach to study the effects of nonrandomized, newly adopted treatments. While covariate adjustment techniques rely on a “no unobserved confounding” assumption, SCQE imposes an assumption on the change in the average nontreatment outcome between successive cohorts (the “baseline trend”). We pr...
Preprint
Full-text available
Understanding how the most severe mass atrocities have historically come to an end may aid in designing policy interventions to more rapidly terminate future episodes. To facilitate research in this area, we construct a new dataset covering all 43 very large mass atrocities perpetrated by governments or non-governments since 1945 with at least 50,0...
Data
Supplement to Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects
Preprint
Full-text available
Are behavioral economics games reliable tools for measuring ethnic biases? Empirical experiences with these tools, together with their vulnerability to self-presentational concerns and insensitivity to more automatic associative or emotional reactions, raise questions as to what biases they might not detect. We recruit experimental subjects in Nair...
Article
Full-text available
Civilians who have fled violent conflict and settled in neighboring countries are integral to processes of civil war termination. Contingent on their attitudes, they can either back peaceful settlements or support warring groups and continued fighting. Attitudes toward peaceful settlement are expected to be especially obdurate for civilians who hav...
Article
Full-text available
One political barrier to climate reforms is the temporal mismatch between short-term policy costs and long-term policy benefits. Will public support for climate reforms increase as climate-related disasters make the short-term costs of inaction more salient? Leveraging variation in the timing of Californian wildfires, we evaluate how exposure to a...
Article
Full-text available
Providing terminally ill patients with access to experimental treatments, as allowed by recent “right to try” laws and “expanded access” programs, poses a variety of ethical questions. While practitioners and investigators may assume it is impossible to learn the effects of these treatment without randomized trials, this paper describes a simple to...
Preprint
Full-text available
Does exposure to violence motivate individuals to support further violence in return, or to seek peace? Answering this question is central to our understanding of how and why conflicts evolve, terminate, and recur. This paper studies the effect of experiencing violence on individual attitudes toward peace in Darfur through a natural experiment base...
Preprint
Full-text available
We introduce trajectory balancing, a general reweighting approach to causal inference with time-series cross-sectional (TSCS) data. We focus on settings in which one or more units is exposed to treatment at a given time, while a set of control units remain untreated throughout a time window of interest. First, we show that many commonly used TSCS m...
Preprint
Full-text available
A method for estimating treatment effects of newly available treatments under arbitrary selection-into-treatment, as illustrated by application to novel medical treatments.
Article
Propensity score matching and weighting are popular methods when estimating causal effects in observational studies. Beyond the assumption of unconfoundedness, however, these methods also require the model for the propensity score to be correctly specified. The recently proposed covariate balancing propensity score (CBPS) methodology increases the...
Preprint
Full-text available
We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that: (i) does not require assumptions about the treatment assignment nor the nature of confounders; (ii) naturally handles multiple confounders, possibly acting non-linearly; (iii) exploits expert knowledge to bound sensitivity paramet...
Article
Full-text available
We introduce trajectory balancing, a general reweighting approach to causal inference withtime-series cross-sectional (TSCS) data. We focus on settings where one or more units is exposed to treatment at a given time, while a set of control units remain untreated. First, we show that many commonly used TSCS methods imply an assumption that each unit...
Preprint
Full-text available
How does regime-inflicted indiscriminate violence affect the political attitudes of refugees from an ongoing civil war? Using a survey of 1,384 Syrian refugees in Turkey, we employ a quasi-experiment based on the inaccuracy of barrel bombs to examine the effect of regime-perpetrated indiscriminate violence on political loyalties. We find that refug...
Article
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The affect misattribution procedure (AMP) is widely used to measure sensitive attitudes towards classes of stimuli, by estimating the effect that affectively charged prime images have on subsequent judgements of neutral target images. We test its resistance to efforts to conceal one's attitudes, by replicating the standard AMP design while offering...
Article
Full-text available
The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without strong functional form assumptions or a specification search. The flexible KRLS estimator learn...
Article
Full-text available
To reduce greenhouse gas emissions in the coming decades, many governments will have to reform their energy policies. These policies are diicult to measure with any precision. As a result, it is unclear whether progress has been made towards important energy policy reforms, such as reducing fossil fuel subsidies. We use new data to measure net taxe...
Article
In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT). Unfortunately, these methods do not necessarily achieve their goal of making the multivariate distribution of covariates for the control group identical to that of the tr...
Preprint
Full-text available
Matching and weighting methods are widely used to estimate causal effects when needing to adjust for a set of observables. Matching is appealing for its nonparametric nature, but with continuous variables, is not guaranteed to remove bias. Weighting techniques choose weights on units to ensure that prespecified functions of the covariates have equa...
Article
This dissertation focuses on the challenges of making inferences from observational data in the social sciences, with particular application to situations of violent conflict. The first essay utilizes quasi-experimental conditions to examine the effects of violence against civilians in Darfur, Sudan on attitudes towards peace and reconciliation. Th...
Article
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This article describes and analyzes patterns of lethal violence in Darfur, Sudan, during 2008-09, drawing upon a uniquely detailed dataset generated by the United Nations-African Union hybrid operation in Darfur (UNAMID), combined with data generated through aggregation of reports from open-source venues. These data enable detailed analysis of patt...
Article
Full-text available
We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial ba...
Article
We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial ba...
Article
The Stata package krls implements Kernel-Based Regularized Least Squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2013) that allows users to solve regression and classification problems without manual specification search and strong functional form assumptions. The flexible KRLS estimator learns the functional form fr...
Article
Full-text available
The usefulness of attentional orienting, both in the real world and in the laboratory, depends not only on the ability to attend to objects or other inputs but also on the ability to shift attention between them. Although understanding the basic characteristics of these shifts is a critical step toward understanding the brain mechanisms that produc...
Article
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Recently, a number of investigators have examined the neural loci of psychological processes enabling the control of visual spatial attention using cued-attention paradigms in combination with event-related functional magnetic resonance imaging. Findings from these studies have provided strong evidence for the involvement of a fronto-parietal netwo...
Article
Various models of executive control predict that practice should modulate the recruitment of executive brain mechanisms. To investigate this issue, we asked 15 participants to perform a cued global/local attention task while brain activity was recorded with event-related functional magnetic resonance imaging (fMRI). Practice significantly reduced t...

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