The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

Latent confounders—unobserved variables that influence both treatment and outcome—can
bias estimates of causal effects. In some cases, these confounders are shared across …

Probabilistic inference provides a powerful theoretical framework for engineering intelligent
systems. However, diverse modeling approaches and inference algorithms are needed to …

A growing family of approaches to causal inference rely on Bayesian formulations of
assumptions that go beyond causal graph structure. For example, Bayesian approaches …

Abstract Reasoning about causal relationships is central to the human experience. This
evokes a natural question in our pursuit of human-like artificial intelligence: how might we …

Observational causal inference (OCI) has shown significant promise in recent years, both as
a tool for improving existing machine learning techniques and as an avenue to aid decision …

Causal inference is a method used in various fields to draw causal conclusions based on
data. It involves using assumptions, study designs, and estimation strategies to minimize the …

I present a new framework and associated synthesis algorithms for program synthesis over
noisy data, ie, data that may contain incorrect/corrupted input-output examples. I model the …