Timezone: »
This abstract describes a survey on deep causal estimators for a social science audience. While the machine learning community has moved quickly to leverage causal reasoning to improve predictive models, adoption of deep learning has been slower in areas of science that prioritize interpretability and robust evidence of causality for inference (e.g., epidemiology, social science, social statistics). Here we summarize deep learning models that adjust for confounding in creative ways (e.g., representation learning and generative modeling) to estimate/predict unbiased treatment effects, and/or extend causal inference beyond tabular data to text and networks. We discuss the strengths and weaknesses of these models from an applied social science perspective, and how the machine learning community might better frame/support their contributions to increase adoption by social and data scientists.
Author Information
Bernard Koch (UCLA)
Niki Kilbertus (Helmholtz AI)
More from the Same Authors
-
2021 Workshop: The Neglected Assumptions In Causal Inference »
Niki Kilbertus · Lily Hu · Laura Balzer · Uri Shalit · Alexander D'Amour · Razieh Nabi -
2021 Poster: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2021 Oral: On Disentangled Representations Learned from Correlated Data »
Frederik Träuble · Elliot Creager · Niki Kilbertus · Francesco Locatello · Andrea Dittadi · Anirudh Goyal · Bernhard Schölkopf · Stefan Bauer -
2018 Poster: Blind Justice: Fairness with Encrypted Sensitive Attributes »
Niki Kilbertus · Adria Gascon · Matt Kusner · Michael Veale · Krishna Gummadi · Adrian Weller -
2018 Oral: Blind Justice: Fairness with Encrypted Sensitive Attributes »
Niki Kilbertus · Adria Gascon · Matt Kusner · Michael Veale · Krishna Gummadi · Adrian Weller -
2018 Poster: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf -
2018 Oral: Learning Independent Causal Mechanisms »
Giambattista Parascandolo · Niki Kilbertus · Mateo Rojas-Carulla · Bernhard Schölkopf