Events

We introduce a framework to formally connect causal inference with tensor completion. In particular, we represent the various potential outcomes (i.e., counterfactuals) of interest through a tensor. The key theoretical results presented are: (i) Formal identification results establishing under what missingness patterns, latent confounding, and structure on the tensor is recovery of unobserved potential outcomes possible. (ii) Introducing novel estimators to recover these unobserved potential outcomes and proving they are finite-sample consistent and asymptotically normal. Through this analysis, we show how to generalize synthetic controls to product counterfactuals under treatment, and how to do entry-wise estimation and inference for matrix completion with missing not at random (MNAR) data. The efficacy of our framework is shown on high-impact applications. These include working with: (i) TaurRx Therapeutics to identify patient sub-populations where their therapy was effective. (ii) Uber Technologies on evaluating the impact of driver engagement policies without running an A/B test. (iii) The Poverty Action Lab at MIT to make personalized policy recommendations to improve childhood immunization rates across villages in Haryana, India.

Yethoven Tukimin (Meta) – Building Immersive Future of Work

ARISE: Venture – Demo Day

Women In Engineering – EPD Lecture Series in partnership with The James Dyson Foundation

DH Asia Webinar Series: New Horizon of Digital Humanities? Computational Social Science and its Application by Dr. Hsuanlei Shao

Consider the following two-person mixed strategy game of a probabilist against Nature with respect to the parameters (f,B,π), where f is a convex function satisfying certain regularity conditions, B is either the set {L_i}_{i=1}^{n} or its convex hull with each L_i being a Markov infinitesimal generator on a finite state space X and π is a given positive discrete distribution on X. The probabilist chooses a prior measure μ within the set of probability measures on B denoted by P(B) and picks a L∈B at random according to μ, whereas Nature follows a pure strategy to select M∈L(π), the set of ππ-reversible Markov generators on X. Nature pays an amount D_f(M||L), the f-divergence from L to M, to the probabilist. We prove that a mixed strategy Nash equilibrium always exists, and establish a minimax result on the expected payoff of the game. This also contrasts with the pure strategy version of the game where we show a Nash equilibrium may not exist. To find approximately a mixed strategy Nash equilibrium, we propose and develop a simple projected subgradient algorithm that provably converges with a rate of O(1/sqrt{t}), where t is the number of iterations. In addition, we elucidate the relationships of Nash equilibrium with other seemingly disparate notions such as weighted information centroid, Chebyshev center and Bayes risk. This talk highlights the powerful interplay and synergy between modern Markov chains theory and geometry, information theory, game theory, optimization and mathematical statistics. This is based on a joint work with Geoffrey Wolfer (RIKEN AIP), and the paper can be found in https://arxiv.org/abs/2310.04115

BUILDSomething 23/24′

Master of Architecture Information Session for Intake 2024

VIE @ SWITCH 2023

In this talk we introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle we obtain a local-to-global paradigm, namely solving a local, i.e., a one time-step robust optimization problem leads to an optimizer of the global (i.e. infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure.