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Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv 2607.14940 ↗First posted 16 Jul 2026cs.LG math.PRcited by 0

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Abstract (arXiv)

We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also impacted through an external field capturing the effects of its treatment as well as latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudo-Likelihood Estimation (MPLE) for learning the model parameters. Under mild assumptions, we establish non-asymptotic consistency for parameter estimation and show this translates to faithful estimation of causal quantities of interest after sampling from the learned model. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.

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References · 20

  1. 2018Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overviewcited by 159
  2. 2019Causal Inference Under Interference And Network Uncertaintycited by 27
  3. 2017Robust Synthetic Controlcited by 25
  4. 2017Auto-G-Computation of Causal Effects on a Networkcited by 16
  5. 2020Randomized Graph Cluster Randomizationcited by 14
  6. 2019Learning from weakly dependent data under Dobrushin's conditioncited by 13
  7. 2019Regression from Dependent Observationscited by 10
  8. 2021A Computationally Efficient Method for Learning Exponential Family Distributionscited by 10
  9. 2022On counterfactual inference with unobserved confoundingcited by 10
  10. 2020On Learning Continuous Pairwise Markov Random Fieldscited by 9
  11. 2021Statistical Estimation from Dependent Datacited by 9
  12. 2019Identification and Estimation of Causal Effects from Dependent Datacited by 8
8 more
  1. 2017Concentration of Multilinear Functions of the Ising Model with Applications to Network Datacited by 5
  2. 2020Logistic-Regression with peer-group effects via inference in higher order Ising modelscited by 5
  3. 2024Doubly Robust Inference in Causal Latent Factor Modelscited by 4
  4. 2024Causal effect estimation under network interference with mean-field methodscited by 3
  5. 2022Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interferencecited by 2
  6. 2024The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interferencecited by 2
  7. 2025Estimating Ising Models in Total Variation Distancecited by 2
  8. 2025Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interferencecited by 1

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