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A Causal Inference Framework for Network Interference with Panel Data
Sarah Cen · Anish Agarwal · Christina Yu · Devavrat Shah

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in Causal Machine Learning for Real-World Impact »
Event URL: https://openreview.net/forum?id=P6ZHSx_H6vo »

We propose a framework for causal inference with panel data in the presence of network interference and unobserved confounding. Key to our approach is a novel latent factor model that takes into account network interference and generalizes the factor models typically used in panel data settings. We propose an estimator–the Network Synthetic Interventions estimator—and show that it consistently estimates the counterfactual outcomes for a unit under an arbitrary set of treatments, if certain observation patterns hold in the data. We corroborate our theoretical findings with simulations. In doing so, our framework extends the Synthetic Control and Synthetic Interventions methods to incorporate network interference.

Author Information

Sarah Cen (Massachusetts Institute of Technology)

PhD student at MIT EECS. Working with Prof. Devavrat Shah in Laboratory for Information and Decision Systems. Research on topics including causal inference and responsible ML. Have previously worked on self-driving cars, robotics, information networks, and multi-armed bandits.

Anish Agarwal (MIT)
Christina Yu (Cornell University)
Devavrat Shah (Massachusetts Institute of Technology)

Devavrat Shah is a professor of Electrical Engineering & Computer Science and Director of Statistics and Data Science at MIT. He received PhD in Computer Science from Stanford. He received Erlang Prize from Applied Probability Society of INFORMS in 2010 and NeuIPS best paper award in 2008.

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