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University of California, Los Angeles
Los Angeles, CA 90095
The Practical Causal Inference (PCI) Lab at UCLA 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 interest lies in causal inference in social science. My substantive research interest includes international political economy and interest group politics.
My research interest is in approaches to generalizability and transportability, particularly with applications in medicine, and the teaching of statistics.
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 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.
My current research interests involve causal effects on physiological diseases and the application and effectiveness of treatments on different patient populations.
My research interest lies in chronic disease epidemiology, with a particular focus on exploring causal association in the presence of incomplete data.
My research interests include adult and child psychiatry, specifically relating to mood disorders, substance use, family dynamics, and unhoused populations.
My research interests include infectious diseases, women’s reproductive health, and occupational health.
My research is in heterogenous treatment effects, specifically in social settings like education research and policy evaluation.
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 research area is reproductive epidemiology with a special focus on early life causes and genetics.
My research interests are primarily in methodological and applied (bio)statistics with a focus on applications of causal inference and machine learning in public and environmental health.
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.
August 2023
Simultaneous 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 adjustmen...August 2023
Monotonicity: 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...Software: Monotonicity Necessity and Sufficiency, Link: Interactive plot for necessary and sufficient regions of monotonicity
July 2023
From “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 causalclaims has increased tremendously in recent years. When relying onobservational data, debate often centers on whether investigators have ruledout any bias due to confoundi...January 2022
Causal 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 prescripti...
University of California, Los Angeles
Los Angeles, CA 90095