{"id":51199,"date":"2025-01-27T15:19:47","date_gmt":"2025-01-27T20:19:47","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=51199"},"modified":"2025-01-30T10:44:26","modified_gmt":"2025-01-30T15:44:26","slug":"ams-special-seminar-series-harsh-parikh","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-special-seminar-series-harsh-parikh\/","title":{"rendered":"AMS Special Seminar Series | Harsh\u00a0Parikh"},"content":{"rendered":"

Location: <\/strong>Clark 110<\/p>\n

When:<\/strong>\u00a0February 4th at 10:00 a.m.<\/p>\n

Title:\u00a0<\/strong>Interpretable Machine Learning & Causal Inference for Advancing Healthcare and Public Health<\/span><\/p>\n

Abstract:\u00a0<\/strong>Causal inference methods are essential across healthcare, public health, and social sciences, helping understand complex systems and inform decision-making. While integrating machine learning (ML) and statistical techniques has improved causal estimation, many of these methods depend on black-box ML approaches. This raises concerns about the communicability, auditability, and trustworthiness of causal estimates, especially in high-stakes contexts. My research addresses these challenges by developing interpretable causal inference methods. In this presentation, I introduce an approach for bridging the research-to-practice gap by generalizing randomized controlled trial (RCT) findings to target populations. Although RCTs are fundamental for understanding causal effects, extending their findings to broader populations is difficult due to effect heterogeneity and the underrepresentation of certain subgroups. Our work tackles this issue by identifying and interpretably characterizing underrepresented subgroups in RCTs. Specifically, we propose the Rashomon Set of Optimal Trees (ROOT), an optimization-based method that produces interpretable characteristics of underrepresented subgroups. This approach helps researchers communicate findings more effectively. We apply ROOT to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial — assessing the effectiveness of opioid use disorder medication — to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations.<\/span><\/p>\n

Bio:<\/strong>\u00a0Harsh\u00a0Parikh\u00a0is a postdoctoral fellow in the Department of Biostatistics at Johns Hopkins University, where he specializes in data fusion techniques to improve transportability and generalizability in high-stakes decision-making. His research focuses on developing interpretable and domain-conscious causal inference models, integrating machine learning to address complex challenges in healthcare, economics, and social science applications. With a Ph.D. in Computer Science from Duke University,\u00a0Harsh’s dissertation on “Causal Inference for High-Stakes Decisions” was supported by the Amazon Fellowship and earned the Outstanding Dissertation Award. His collaborative work includes efforts with institutions such as Massachusetts General Hospital, Beth Israel Deaconess Center, Columbia Mailman School of Public Health, and Amazon.<\/span><\/p>\n

Zoom link:<\/strong> https:\/\/wse.zoom.us\/j\/93287142219?pwd=z9fqWnRMzmzS0SGijRiie5yN3kHRSZ.1<\/a><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Location: Clark 110 When:\u00a0February 4th at 10:00 a.m. Title:\u00a0Interpretable Machine Learning & Causal Inference for Advancing Healthcare and Public Health Abstract:\u00a0Causal inference methods are essential across healthcare, public health, 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