Chandler Squires

Chandler Squires

PhD Candidate



I am a final year PhD student in EECS at MIT. I am currently applying for faculty positions in Statistics and Computer Science (as of Fall 2023). I am extremely fortunate to be advised by Caroline Uhler and David Sontag.

Ny research focuses on building the statistical and computational foundations for AI-driven decision-making in scientific applications, and I work on methods for causal structure learning, experimental design, and causal representation learning.

As a member of the Eric and Wendy Schmidt Center, my work is grounded by problems in cellular biology, e.g. predicting the effect of a drug in novel cancer cell lines.

Teaching and Organizing. I really enjoy teaching, mentoring, and organizing academic initiatives. Check out the lecture notes and recordings for the causality class that I developed for MIT’s 2023 January term, and join the ongoing talk series, Causality, Abstraction, Reasoning, and Extrapolation (CARE), that I am co-organizing.

Communication. I believe that communicating technical material is one of the most important and most challenging activities in a research career. As a member of the EECS Communication Lab, I coach researchers on how to communicate more effectively.

Software. I like writing good, easy-to-use code (when time permits 😅), and I am the main developer of causaldag, a Python package for creating, manipulating, and learning causal graphical models. Shoot me an email to chat about the package or my work!

Other materials. I try to keep an up-to-date repository of slides from my talks and posters from conferences and workshops.

  • Causal Structure Learning
  • Experimental Design
  • Causal Representation Learning
  • High-Dimensional Statistics and Semiparametric Efficiency
  • Computational Biology/Genomics
  • MEng in Electrical Engineering and Computer Science, 2019

    Massachusetts Institute of Technology

  • BSc in Electrical Engineering and Computer Science, 2018

    Massachusetts Institute of Technology

All Publications

Active Learning for Optimal Intervention Design in Causal Models

An important problem across disciplines is the discovery of interventions that produce a desired outcome. When the space of possible interventions is large, making an exhaustive search infeasible, experimental design strategies are needed. In this context, encoding the causal relationships between the variables, and thus the effect of interventions on the system, is critical in order to identify desirable interventions efficiently. We develop an iterative causal method to identify optimal interventions, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. We formulate an active learning strategy that uses the samples obtained so far from different interventions to update the belief about the underlying causal model, as well as to identify samples that are most informative about optimal interventions and thus should be acquired in the next batch. The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for efficient optimization. The resulting algorithms are theoretically grounded with information-theoretic bounds and provable consistency results. We illustrate the method on both synthetic data and real-world biological data, namely gene expression data from Perturb-CITE-seq experiments, to identify optimal perturbations that induce a specific cell state transition; the proposed causal approach is observed to achieve better sample efficiency compared to several baselines. In both cases we observe that the causally informed acquisition function notably outperforms existing criteria allowing for optimal intervention design with significantly less experiments.