University of California, Los Angeles
Los Angeles, CA 90095
The Gaussian Process (GP) is a modeling tool that elegantly combines a highly flexible but easy to understand approach to non-linear regression with rigorous handling of uncertainty estimation. Unlike conventional approaches, it produces uncertainty estimates that reflect uncertainty over the true model and which consequently grow wider in areas with less data.
We offer an accessible explanation of GPs for social scientists and an estimation approach (with software) that avoids most user-driven hyperparameter tuning. These approaches produce more appropriate confidence intervals for conditional or pointwise estimates under poor overlap, and produce reliable inferences for approaches involving prediction at or beyond the edge of training data, such as regression discontinuity designs.
The U.S. leads the world in the number of violent mass shootings that occur each year, and yet policy making on gun violence prevention efforts remains stagnant and polarized along party lines. Are legislators responsive to mass shootings? To answer this, we aim to utilize modern methods of causal inference to investigate whether the occurrence of a mass shooting within or near a legislator's district affects their voting behavior on gun policy measures. We will pair a dataset on all 111 public mass shootings from 2000 to 2022 with an independently collected dataset of state legislators’ roll-call voting records on the over 15,000 gun-related bills introduced in state legislatures during this period. We seek to shed light on the manners by which the government responds – or fails to respond – to mass shootings, and how proximity to these events affects legislative decision-making, and how these decisions affect public health outcomes.
What could have or should have been learned about the effects of experimental COVID therapies from their uses outside of randomized trials? Such questions are fraught because patients and physicians need to make decisions with the available information, but such observational evidence is vulnerable to misleading biases. We consider the “stability controlled quasi-experiment,” which shows valid treatment effect estimates for any given assumption regarding the “baseline trend” (how much the average outcome, absent treatment, would have shifted on its own between successive cohorts). We investigate the effects of three therapies (remdesivir, dexamethasone, and hydroxychloroquine) using small samples from outside of randomized trials. We find that over the range of baseline trend assumptions we argue to be plausible, the corresponding range of causal estimates is wide but informative and consistent with the results of eventual randomized trials.
In observational and experimental settings, researchers have often used regression to estimate an average treatment effect (ATE) while adjusting for observables (X). Since at least Angrist (1998), we have known that the regression coefficient produces an awkwardly weighted average of strata-wise treatment effects not generally equal to the ATE. We demystify this weighting, which is best understood as merely a symptom of an OLS specification ill-equipped to handle heterogeneous effects. Several natural solutions—imputation/g-computation, interacting X with the treatment, and mean balancing—sidestep this problem and offer very simple alternatives to simply regressing Y on D and X. We provide new derivations of these weights, show the exact equivalence of imputation and interaction estimators, describe the underlying assumptions required for these alternatives to work, and illustrate the consequences of these findings for estimation in both observational and experimental designs.
Many studies in the social sciences use close elections to test what happens when certain types of politicians get elected, such as men vs. women. In this project, Chad Hazlett and Andrew Bertoli are working to clarify what can be learned from these designs and to outline methodological tools that can be employed in this context. The project has important implications for studying leader effects from close elections.
This research project examines the impact of a community college's online winter term on enrollment and student outcomes using a variety of causal inference approaches (e.g., instrumental variable in randomized encouragement, stability controlled quasi-experiment, and selection on observables).
Researchers often attempt to estimate the effect of a treatment on an outcome within a sample that has been drawn in some selective way from a larger population. Such selective sampling may not only change the population about which we make inferences, but can bias our estimate of the causal effect even for the units in the sample, thus threatening the "internal validity'' of the estimate. We propose "internal selection graphs", annotating standard graphs to show the impacts of selection. We then extend standard criteria (backdoor/adjustment) so that users can determine what effects are identifiable, and what to condition on.
Placebo treatments and outcomes can be useful when attempting to make causal claims from observational data. Existing approaches, however, rely on two strong assumptions: (i) "perfect placebos", meaning placebo treatments have precisely zero effect on the outcome and the real treatment has precisely zero effect on a placebo outcome; and (ii) "equiconfounding", meaning that the treatment-outcome relationship where one is a placebo suffers the same amount of confounding as does the real treatment-outcome relationship, on some scale. We consider how these assumptions can be relaxed, within linear models. While applicable in many settings, we note that this also offers a relaxation for the difference-in-difference framework, taking the pre-treatment outcome as a placebo outcome, but relaxing the parallel trends (equiconfounding) assumption.
The Practical Causal Inference (PCI) Lab advances and applies methods that allow researchers and practitioners to make “safer” and more realistic causal conclusions in real-world scenarios. Such methods extract available useful information while reducing risk of generating over-confidence in a possibly badly biased result. We refer to this approach as “practical causal inference.”
The PCI Lab serves as a multidisciplinary intellectual hub, producing research, providing training and mentorship, and spearheading collaborative initiatives across campus and beyond.
Professors Onyebuchi Arah and Chad Hazlett lead an interdisciplinary team of students, postdoctoral scholars, and early-career faculty dedicated to advancing the field of causal inference and its practical application. They mentor students and postdoctoral scholars in the health, social, and physical sciences, including statistics, political science, epidemiology, biostatistics, education, communications, sociology, medicine, and computer science.
Research interests include bounds, policies, and decision making on Probabilities of Causation, monotonicity, and selection bias.
My research interests include social policy evaluation, causal inference methods, early childhood development, family well-being, early adversity prevention, and structural inequality. The aim of my research is to understand how public policies and other structural factors shape early foundations for healthy outcomes over the lifespan.
My research interest lies in causal inference in social science. My substantive research interest includes international political economy and interest group politics.
I am interested in using causal inference methods to better model potential impact of interventions and policies on population health. I am also interested in methods involving simulation for causal inference, including quantitative bias analysis and systems science modeling.
I am inspired by all things causal inference, with an inclination towards transportability analysis. Transportability analysis provides a framework for generating causal estimates for any target population of interest, which can be beneficial for gaining insights into the health of data-poor and often understudied populations.
My research interest is in approaches to generalizability and transportability, particularly with applications in medicine, and the teaching of statistics.
I am broadly interested in causal inference and machine learning methods for the social sciences. My research projects include partial identification of causal quantities and causal inference with survey and voting data.
I'm a Ph.D. student enrolled at Aarhus University in Denmark. In my research I use the Danish National Birth Cohort and the Danish registers to investigate the association between parental socioeconomic factors and reproductive health in children using causal inference methods.
My primary interests include reproductive epidemiology, causal inference methods, and dermatology. During my PhD, I am researching the relationship between atopic dermatitis and various reproductive health outcomes.
My current research focuses on causal effect estimation and applications of causal methods in public policy settings.
Mixed methods inquiry, academic and labor market experiences of community college students, historically minoritized communities, equity and justice.
I study American Politics and Methodology. My broad research interests are in representation, political behavior, and public policy in state and local governments in the United States, centering on studying the politics of firearm ownership and firearm-based violence and suicide.
I use machine learning and causal inference tools to study the political economy of U.S. elections.
My current research interests include developing new models for causal inference and synthetic data generation using tools from transfer metric learning and optimal transport.
I have a master’s degree in health science and a great passion for epidemiological research. Currently, I study how pubertal development affects mental health problems in adolescence.
I study how real-world events interact with voters’ partisan attachments to shape elections and public opinion in the United States. I am broadly interested in electoral accountability, political economy, and political psychology, especially when these topics intersect with public health and gun violence.
The goal of my research is to develop and utilize innovative and rigorous epidemiologic, econometric and causal inference methods, as well as computational modeling and simulation tools for investigating the impact of lifestyle, metabolic and social interventions in preventing chronic diseases.
I am interested in generalizing results of substance use treatment clinical trials to people with multiple co-occurring mental health disorders.
My research interests are in Development Economics, Labour Economics, and Girls Education, Gender Equality.
My research explores the social and political effects of modern sports. I also work to improve social science methods for causal inference, in particular regression discontinuity analysis and survey designs.
I am interested in target trial emulation from observational data and the application of causal inference to questions about aging physiology.
My key contributions to science are in the field of reproductive epidemiology with a strong focus on the potential effects of prenatal and early life exposures on pubertal development, semen quality, fecundity, fertility and infertility. I have extensive expertise in conducting epidemiologic studies in large birth cohorts and nationwide Danish/Nordic registries by using causal inference methods.
My research interests include developing tools that aid practitioners in making more credible causal inferences, sample selection as a threat to both internal and external validity, placebo methods, and the connections between causal frameworks and across identification strategies.
I study causal inference in observational and quasi-experimental settings, with a focus on identifying the effects of social inequality on people's life outcomes.
I am an infectious diseases epidemiologists, with research interests in maternal, perinatal and pediatric vaccine program evaluation. I have considerable experience in the use of linked data to support communicable disease surveillance and control, and in overseeing public health intelligence programs to support policy development and implementation.
August 2023Simultaneous adjustment of uncontrolled confounding, selection bias and misclassification in multiple-bias modelling International Journal of Epidemiology
Adjusting for multiple biases usually involves adjusting for one bias at a time, with careful attention to the order in which these biases are adjusted. A novel, alternative approach to multiple-bias adjustment involves the simultaneous adjustment of all biases via imputation and/or regression weighting. The imputed value or weight corresponds to the probability of the missing data and serves to 'reconstruct' the unbiased data that would be observed based on the provided assumptions of the degree of bias.
August 2023Monotonicity: Detection, Refutation, and Ramification
The assumption of monotonicity, namely that outputs cannot decrease when inputs increase, is critical for many reasoning tasks, including unit selection, A/B testing, and quasi-experimental econometrics. It is also vital for identifying Probabilities of Causation, which, in turn, enable the estimation of individual-level behavior. This paper demonstrates how monotonicity can be detected (or refuted) using observational, experimental, or combined data. Using such data, we pinpoint regions where monotonicity is definitively violated, where it unequivocally holds, and where its status remains undetermined. We further explore the consequences of monotonicity violations, especially when a maximum percentage of possible violation is specified. Finally, we illustrate applications for personalized decision-making.
July 2023From “Is it unconfounded?” to “How much confounding would it take?”: Applying the sensitivity-based approach to assess causes of support for peace in Colombia The Journal of Politics
Attention to the credibility of causal
claims has increased tremendously in recent years. When relying on
observational data, debate often centers on whether investigators have ruled
out any bias due to confounding. However, the relevant
scientific question is generally not whether bias is precisely zero, but
whether it is problematic enough to alter one’s research conclusion. We argue
that sensitivity analyses would improve research practice by showing how
results would change under plausible degrees of confounding, or equivalently,
by revealing what one must argue about the strength of confounding to sustain a
research conclusion. This would improve scrutiny of studies in which non-zero
bias is expected, and of those where authors argue for zero bias but results
may be fragile to confounding too weak to be ruled out. We illustrate this
using off-the-shelf sensitivity tools to examine two potential influences on
support for the FARC peace agreement in Colombia.
January 2022Causal Effect of Chronic Pain on Mortality Through Opioid Prescriptions: Application of the Front-Door Formula Epidemiology
Background: Chronic pain is the leading cause of disability worldwide and is strongly associated with the epidemic of opioid overdosing events. However, the causal links between chronic pain, opioid prescriptions, and mortality remain unclear.
Methods: This study included 13,884 US adults aged ≥20 years who provided data on chronic pain in the National Health and Nutrition Examination Survey 1999-2004 with linkage to mortality databases through 2015. We employed the generalized form of the front-door formula within the structural causal model framework to investigate the causal effect of chronic pain on all-cause mortality mediated by opioid prescriptions.
Results: We identified a total of 718 participants at 3 years of follow-up and 1260 participants at 5 years as having died from all causes. Opioid prescriptions increased the risk of all-cause mortality with an estimated odds ratio (OR) (95% confidence interval) = 1.5 (1.1, 1.9) at 3 years and 1.3 (1.1, 1.6) at 5 years. The front-door formula revealed that chronic pain increased the risk of all-cause mortality through opioid prescriptions; OR = 1.06 (1.01, 1.11) at 3 years and 1.03 (1.01, 1.06) at 5 years. Our bias analysis showed that our findings based on the front-door formula were likely robust to plausible sources of bias from uncontrolled exposure-mediator or mediator-outcome confounding.
Conclusions: Chronic pain increased the risk of all-cause mortality through opioid prescriptions. Our findings highlight the importance of careful guideline-based chronic pain management to prevent death from possibly inappropriate opioid prescriptions driven by chronic pain.
University of California, Los Angeles
Los Angeles, CA 90095