Timezone: »
We address the problem of using observational data to estimate peer contagion effects, the influence of treatments applied to individuals in a network on the outcomes of their neighbours. A main challenge to such estimation is that homophily - the tendency of connected units to share similar latent traits - acts as an unobserved confounder for contagion effects. Informally, it's hard to tell whether your friends have similar outcomes because they were influenced by your treatment, or whether it's due to some common trait that caused you to be friends in the first place. Because these common causes are not usually directly observed, they cannot be simply adjusted for. We describe an approach to perform the required adjustment using node embeddings learned from the network itself. The main aim is to perform this adjustment non-parametrically, without functional form assumptions on either the process that generated the network or the treatment assignment and outcome processes. The key questions we address are: How should the causal effect be formalized? And, when can embedding methods yield causal identification?
Author Information
Irina Cristali (University of Chicago)
Victor Veitch (University of Chicago, Google)
More from the Same Authors
-
2021 Spotlight: Counterfactual Invariance to Spurious Correlations in Text Classification »
Victor Veitch · Alexander D'Amour · Steve Yadlowsky · Jacob Eisenstein -
2021 : Using Embeddings to Estimate Peer Influence on Social Networks »
Irina Cristali · Victor Veitch -
2021 : Mitigating Overlap Violations in Causal Inference with Text Data »
Lin Gui · Victor Veitch -
2022 : Causal Estimation for Text Data with (Apparent) Overlap Violations »
Lin Gui · Victor Veitch -
2022 Poster: Using Embeddings for Causal Estimation of Peer Influence in Social Networks »
Irina Cristali · Victor Veitch -
2022 Poster: Invariant and Transportable Representations for Anti-Causal Domain Shifts »
Yibo Jiang · Victor Veitch -
2021 Poster: Counterfactual Invariance to Spurious Correlations in Text Classification »
Victor Veitch · Alexander D'Amour · Steve Yadlowsky · Jacob Eisenstein -
2020 Poster: Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding »
Victor Veitch · Anisha Zaveri -
2020 Spotlight: Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding »
Victor Veitch · Anisha Zaveri -
2019 : Coffee break, posters, and 1-on-1 discussions »
Julius von Kügelgen · David Rohde · Candice Schumann · Grace Charles · Victor Veitch · Vira Semenova · Mert Demirer · Vasilis Syrgkanis · Suraj Nair · Aahlad Puli · Masatoshi Uehara · Aditya Gopalan · Yi Ding · Ignavier Ng · Khashayar Khosravi · Eli Sherman · Shuxi Zeng · Aleksander Wieczorek · Hao Liu · Kyra Gan · Jason Hartford · Miruna Oprescu · Alexander D'Amour · Jörn Boehnke · Yuta Saito · Théophile Griveau-Billion · Chirag Modi · Shyngys Karimov · Jeroen Berrevoets · Logan Graham · Imke Mayer · Dhanya Sridhar · Issa Dahabreh · Alan Mishler · Duncan Wadsworth · Khizar Qureshi · Rahul Ladhania · Gota Morishita · Paul Welle -
2019 Poster: Using Embeddings to Correct for Unobserved Confounding in Networks »
Victor Veitch · Yixin Wang · David Blei -
2019 Poster: Adapting Neural Networks for the Estimation of Treatment Effects »
Claudia Shi · David Blei · Victor Veitch -
2015 : The general class of (sparse) random graphs arising from exchangeable point processes »
Victor Veitch