My current research aims to develop machine learning techniques that will bridge the gap between randomized experiments and empirical inference, enabling machines to better learn causality from data. It has numerous applications in observational studies, medical diagnosis, economics, and online advertisement, for example. To this end, I am employing tools and analyses from related disciplines including but not limited to

• Statistical learning theory
• Kernels and reproducing kernel Hilbert spaces (RKHSs)
• Hilbert space embedding of probability distributions
• Potential outcome framework and Rubin's causal model

In general, I aim to address the most fundamental problems in machine learning and to leverage such insights in solving real-world problems in related disciplines. You can find more information about me and my work at http://krikamol.org.

Kernel methods Observational studies Causal inference RKHS