3 citations · 4 across the 8 of their papers we have counts for
11 papers
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…
Foundation Models for Partial Causal Identification
Alexis Bellot, Anish Dhir
This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data. We show that a canonical p…
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…
The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models
Valentinian Lungu, Anish Dhir, Mark van der Wilk +1
We investigate the asymptotic properties of Bayesian bivariate causal discovery for Gaussian Linear Structural Equation Models (SEMs) with heteroscedastic noise. We demonstrate tha…
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…
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori +2
Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not…
Bivariate Causal Discovery using Bayesian Model Selection
Anish Dhir, Samuel Power, Mark van der Wilk
Much of the causal discovery literature prioritises guaranteeing the identifiability of causal direction in statistical models. For structures within a Markov equivalence class, th…
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…
Integrating overlapping datasets using bivariate causal discovery
Anish Dhir, Ciarán M. Lee
Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can…