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Found 9 papers, 7 papers with code
Recursive Causal Discovery
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
CausalCite: A Causal Formulation of Paper Citations
Evaluating the significance of a paper is pivotal yet challenging for the scientific community.
s-ID: Causal Effect Identification in a Sub-Population
We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population).
Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables
We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables.
Revisiting the General Identifiability Problem
A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles.
Causal Effect Identification with Context-specific Independence Relations of Control Variables
We study the problem of causal effect identification from observational distribution given the causal graph and some context-specific independence (CSI) relations.
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case.
A Recursive Markov Boundary-Based Approach to Causal Structure Learning
In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature.
