I am a postdoc researcher at the University of Copenhagen, working with Niels Richard Hansen in the Copenhagen Causality Lab, Department of Mathematical Sciences. I’m also a SMARTbiomed fellow working with Erin Gabriel in the Section for Biostatistics.

My research focuses on causal machine learning, including discovery, inference, representation learning, and applications; it ranges from foundational work intersecting combinatorics and algebraic statistics, to developing causal deep generative models, to applications in neuroimaging and single-cell transcriptomics.

I was previously a postdoc at KTH Royal Institute of Technology in Stockholm, working with Liam Solus in the Department of Mathematics; before that I earned my PhD in computer science, advised by Moritz Grosse-Wentrup in the Research Group Neuroinformatics at the University of Vienna, and an MS in Logic, Computation, and Methodology at Carnegie Mellon University, advised by David Danks.

• <2026-06-20 Sat> I'll give an invited talk at the Biological Applications of Computer Algebra session of ICMS'26
• <2026-04-16 Thu> I'll give a contributed talk at EuroCIM'26
• <2026-03-04 Wed> I'll present a poster in the Causal identification and discovery workshop at the Isaac Newton Institute
• <2025-12-07 Sun> I'm co-organizing the Causality for Impact workshop at EurIPS'25
• <2025-07-25 Fri> I'll present two posters at the Causal Abstractions and Representations workshop at UAI'25

• Intervening to learn and compose causally disentangled representations. Alex Markham, Isaac Hirsch, Jeri A. Chang, Liam Solus, Bryon Aragam. arXiv:2507.04754 [stat.ML].
• Coarsening causal DAG models. Francisco Madaleno, Pratik Misra, Alex Markham. arXiv:2601.10531 [stat.ML].
• Scalable structure learning for sparse context-specific systems. Felix Leopoldo Rios, Alex Markham, Liam Solus. arXiv:2402.07762 [stat.ML].

• Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks. Danai Deligeorgaki, Alex Markham, Pratik Misra, Liam Solus. Algebraic Statistics. 2024.
• A transformational characterization of unconditionally equivalent Bayesian networks. Alex Markham, Danai Deligeorgaki, Pratik Misra, Liam Solus. Probabilistic Graphical Models (PGM). 2022.
• A distance covariance-based kernel for nonlinear causal clustering in heterogeneous populations. Alex Markham, Richeek Das, Moritz Grosse-Wentrup. Causal Learning and Reasoning (CLeaR). 2022.
• Measurement dependence inducing latent causal models. Alex Markham, Moritz Grosse-Wentrup. Uncertainty in Artificial Intelligence (UAI). 2020.