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Found 13 papers, 8 papers with code
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
We study the index-only setting, where the actions and contexts are categorical variables with a finite number of possible values.
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model.
Linear Causal Disentanglement via Interventions
A representation is identifiable if both the latent model and the transformation from latent to observed variables are unique.
Active Learning for Optimal Intervention Design in Causal Models
Here, we develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean.
Causal Structure Learning: a Combinatorial Perspective
In this review, we discuss approaches for learning causal structure from data, also called causal discovery.
Matching a Desired Causal State via Shift Interventions
In particular, we show that our strategies may require exponentially fewer interventions than the previously considered approaches, which optimize for structure learning in the underlying causal graph.
Active Structure Learning of Causal DAGs via Directed Clique Trees
Most existing works focus on \textit{worst-case} or \textit{average-case} lower bounds for the number of interventions required to orient a DAG.
Efficient Permutation Discovery in Causal DAGs
We compare our method with $w = 1$ to algorithms for finding sparse elimination orderings of undirected graphs, and show that taking advantage of DAG-specific problem structure leads to a significant improvement in the discovered permutation.
Active Structure Learning of Causal DAGs via Directed Clique Tree
Most existing works focus on worst-case or average-case lower bounds for the number of interventions required to orient a DAG.
Ordering-Based Causal Structure Learning in the Presence of Latent Variables
We consider the task of learning a causal graph in the presence of latent confounders given i. i. d.~samples from the model.
Size of Interventional Markov Equivalence Classes in Random DAG Models
For constant density, we show that the expected $\log$ observational MEC size asymptotically (in the number of vertices) approaches a constant.
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.
Methodology
Direct Estimation of Differences in Causal Graphs
We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models given i. i. d.~samples from each model.
Methodology
