3 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
Yuche Gao, Arik Reuter, Siyuan Guo +3
Causal foundation models (CFMs) amortise causal inference over priors of synthetic structural causal models (SCMs), predicting the effect of an experiment on a specific variable. H…
PRIM: Meta-Learned Bayesian Root Cause Analysis
Christopher Lohse, Anish Dhir, Amadou Ba +3
Root cause analysis (RCA) in complex systems is challenging due to error propagation across multiple variables, the need for structural causal knowledge, and the computational cost…
Use What You Know: Causal Foundation Models with Partial Graphs
Arik Reuter, Anish Dhir, Cristiana Diaconu +6
Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions. Recently proposed Causal Foundation Models (CFMs) promise a more unified a…
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Anish Dhir, Cristiana Diaconu, Valentinian Mihai Lungu +3
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the ca…
A Meta-Learning Approach to Bayesian Causal Discovery
Anish Dhir, Matthew Ashman, James Requeima +1
Discovering a unique causal structure is difficult due to both inherent identifiability issues, and the consequences of finite data. As such, uncertainty over causal structures, su…
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
Alexis Bellot, Anish Dhir, Giulia Prando
We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relatio…