If you're interested in working with me, feel free to reach out at [email protected].

Looking ahead, my future direction will explore:
1️⃣ High-dimensional, online streaming datasets.
2️⃣ Multi-modal data (e.g., text, images).
3️⃣ Robust causal inference with uncertainty quantification.

My past work focuses on estimating causal effects identifiable from graphs, with applications in xAI and healthcare. This includes advancing methods to handle multi-domain experimental data, distributional treatment effects, and designing computationally efficient estimators.

In sum, our work provides a computationally efficient and statistically robust estimator for various covariate adjustment estimands, including cases where no such estimators previously existed.

Come see our poster and let us chat more!

Next, we developed Double-machine learning (DML)-based estimators for the UCA-class and provided finite sample guarantees, showing that it achieves doubly robustness and scalability (i.e., computational efficiency).

The UCA class incorporates a functional in a form of a product of various conditional probabilities. It includes the front-door adjustment, Verma’s equation, S-admissibility, Effect of treatment on the treated, soft-intervention, and many other practical causal estimands.

In this work,
1. We define a function class called "Unified Covariate Adjustment (UCA)" that incorporates various covariate adjustments; and
2. We developed a double machine learning (DML)-based estimator for the UCA-classes and provided finite-sample learning guarantees.