Harsh Parikh

My research focuses on developing (interpretable) causal inference approaches for aiding decisions in high-stakes complex scenarios. My collaborators and I have used my research to address challenges in healthcare, public health, and social sciences. Decision-making in these critical domains is fraught with difficulties stemming from, but not limited to, the intricate interplay of factors, including the heterogeneity of causal effects across subpopulations, the substantial costs associated with suboptimal decisions, and the inherent complexities in the available data, all of which complicate the assessment of risk-benefit trade-offs. In pursuit of more effective solutions, my work is centered around the development of causal inference methodologies that are:

Accurate: to ensure accurate estimation of heterogeneous causal effects, even in scenarios with data limitations, offering decision-makers a reliable foundation upon which to base their choices.

Trustworthy: to empower domain experts to comprehend the inner workings of the causal inference process. This not only enables experts to validate the underlying assumptions but also guarantees patients' safety.

Domain-conscious: to bridge the research-to-practice gap and yield solutions that are readily implementable. I leverage the context and domain knowledge to tailor solutions specific to a subject matter.