65 citations · 71 across the 12 of their papers we have counts for
12 papers
I-FLOP: Fast Learning of Order and Parents from Interventional Data
Liuting Chen, Alex Markham
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienöbst et al. (2026) from observational to interventional data. In particular, we use the i…
Coarsening Linear Non-Gaussian Causal Models with Cycles
Francisco Madaleno, Francisco C Pereira, Alex Markham
Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal struct…
Coarsening Causal DAG Models
Francisco Madaleno, Pratik Misra, Alex Markham
Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across…
Intervening to Learn and Compose Causally Disentangled Representations
Alex Markham, Isaac Hirsch, Jeri A. Chang +2
In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this pa…
Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling
Xudong Sun, Alex Markham, Pratik Misra +1
Unbiased data synthesis is crucial for evaluating causal discovery algorithms in the presence of unobserved confounding, given the scarcity of real-world datasets. A common approac…
Scalable Structure Learning for Sparse Context-Specific Systems
Felix Leopoldo Rios, Alex Markham, Liam Solus
Several approaches to graphically representing context-specific relations among jointly distributed categorical variables have been proposed, along with structure learning algorith…
Neuro-Causal Factor Analysis
Alex Markham, Mingyu Liu, Bryon Aragam +1
Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is…
Queer In AI: A Case Study in Community-Led Participatory AI
Organizers Of QueerInAI, :, Anaelia Ovalle +48
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community…
Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks
Danai Deligeorgaki, Alex Markham, Pratik Misra +1
We consider the problem of estimating the marginal independence structure of a Bayesian network from observational data, learning an undirected graph we call the unconditional depe…
A Transformational Characterization of Unconditionally Equivalent Bayesian Networks
Alex Markham, Danai Deligeorgaki, Pratik Misra +1
We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i.e., when directed acyclic graphs (DAGs) have the same set of unconditional -separa…
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
Alex Markham, Richeek Das, Moritz Grosse-Wentrup
We consider the problem of causal structure learning in the setting of heterogeneous populations, i.e., populations in which a single causal structure does not adequately represent…
Measurement Dependence Inducing Latent Causal Models
Alex Markham, Moritz Grosse-Wentrup
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theo…