ENBIS/JMP webinar: Two paths to causal inference: Propensity scores and structural equation modeling
Two paths to causal inference: Propensity scores and structural equation modeling
Speaker:
Meichen Dong, JMP, Senior Research Statistician Developer
Laura Castro-Schilo, JMP, Senior Research Statistician Developer
Date: Dec 9th, 3 pm CET
Abstract:
At the heart of many science and engineering problems is a causal question: Does X cause Y, and if so, what is the magnitude and direction of the effect in the presence of other effects? Although well-designed experiments are the gold standard for estimating causal effects, many situations make experiments impossible or unethical to conduct. Moreover, much of modern data are observational, gathered from a variety of sources like sensors or surveys. Thus, learning how to make causal inferences from these data is critical for solving a variety of problems. Because causal inference has a rich history in many disciplines, different terminologies and methods exist. This presentation focuses on two complimentary approaches: propensity score methods and structural equation modeling. We clarify key causal concepts and rely on illustrative examples to perform causal inference using JMP Pro.
Bios:
Meichen Dong is a Senior Research Statistician Developer at JMP Statistical Discovery. Her research focus includes causal inference, genetics, multi-omics analysis, high-dimensional data analysis, and clinical trials. She holds a Ph.D. in biostatistics from the University of North Carolina at Chapel Hill, contributing her expertise to advance life sciences.
Laura Castro-Schilo is a Senior Research Statistician Developer at JMP, leading the development of the structural equation modeling (SEM) platform. She obtained her Ph.D. in quantitative psychology from the University of California, Davis and has authored over two dozen publications on methodology and applications of SEM. Previously, she was an Assistant Professor at the L. L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill.