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 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. T
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- FuzzySimilar title/name (fuzzy) · 84%xorbitsai/inference →
“Fuzzy title match (0.92): “Causal Inference for Sequential Settings under Interference ” ≈ “xorbitsai/inference””
- LinkedLinked via arxiv author · 85%Phevos Paschalidis →
“Causal Inference for Sequential Settings under Interference and Latent Confounding”
- LinkedLinked via arxiv author · 85%Constantinos Daskalakis →
“Causal Inference for Sequential Settings under Interference and Latent Confounding”
- LinkedLinked via arxiv author · 85%Devavrat Shah →
“Causal Inference for Sequential Settings under Interference and Latent Confounding”