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New method for causal inference in sequential settings with interference

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for causal inference in sequential settings with interference and latent confounding. The approach utilizes an Ising model to capture dependencies between unit outcomes over time, incorporating treatment effects and latent confounders. A computationally efficient Maximum Pseudo-Likelihood Estimation (MPLE) method is proposed for parameter learning, with theoretical guarantees of non-asymptotic consistency. The method's effectiveness is demonstrated through synthetic experiments and a real-world case study on COVID-19 vaccine rates and death rates in US counties. AI

IMPACT This research could improve the accuracy of causal effect estimations in complex, real-world scenarios, potentially impacting fields like public health and policy-making.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a new research methodology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method for causal inference in sequential settings with interference

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The cluster contains an academic paper published on arXiv detailing a new research methodology.
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94 days old
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah ·

    Causal Inference for Sequential Settings under Interference and Latent Confounding

    arXiv:2607.14940v1 Announce Type: new Abstract: 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 outcome…

  2. arXiv cs.LG TIER_1 English(EN) · Devavrat Shah ·

    Causal Inference for Sequential Settings under Interference and Latent Confounding

    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 …

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