# Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv: https://arxiv.org/abs/2607.14940 · HTML: https://mlsanity.com/paper/2607.14940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 20 of 20)

- [Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview](https://mlsanity.com/paper/1809.09573/) (2018)
- [Causal Inference Under Interference And Network Uncertainty](https://mlsanity.com/paper/1907.00221/) (2019)
- [Robust Synthetic Control](https://mlsanity.com/paper/1711.06940/) (2017)
- [Auto-G-Computation of Causal Effects on a Network](https://mlsanity.com/paper/1709.01577/) (2017)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [Learning from weakly dependent data under Dobrushin's condition](https://mlsanity.com/paper/1906.09247/) (2019)
- [Regression from Dependent Observations](https://mlsanity.com/paper/1905.03353/) (2019)
- [A Computationally Efficient Method for Learning Exponential Family Distributions](https://mlsanity.com/paper/2110.15397/) (2021)
- [On counterfactual inference with unobserved confounding](https://mlsanity.com/paper/2211.08209/) (2022)
- [On Learning Continuous Pairwise Markov Random Fields](https://mlsanity.com/paper/2010.15031/) (2020)
- [Statistical Estimation from Dependent Data](https://mlsanity.com/paper/2107.09773/) (2021)
- [Identification and Estimation of Causal Effects from Dependent Data](https://mlsanity.com/paper/1902.01443/) (2019)
- [Concentration of Multilinear Functions of the Ising Model with Applications to Network Data](https://mlsanity.com/paper/1710.04170/) (2017)
- [Logistic-Regression with peer-group effects via inference in higher order Ising models](https://mlsanity.com/paper/2003.08259/) (2020)
- [Doubly Robust Inference in Causal Latent Factor Models](https://mlsanity.com/paper/2402.11652/) (2024)
- [Causal effect estimation under network interference with mean-field methods](https://mlsanity.com/paper/2407.19613/) (2024)
- [Estimating Ising Models in Total Variation Distance](https://mlsanity.com/paper/2511.21008/) (2025)
- [Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference](https://mlsanity.com/paper/2210.11355/) (2022)
- [The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interference](https://mlsanity.com/paper/2411.10908/) (2024)
- [Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference](https://mlsanity.com/paper/2502.13238/) (2025)

## 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.