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Estimating Causal Effects with Neural Networks

Published on:

17 August 2020

Primary Category:

Methodology

Paper Authors:

Sergio Garrido,

Stanislav S. Borysov,

Jeppe Rich,

Francisco C. Pereira

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Key Details

Proposes neural networks to estimate causal effects without functional form assumptions

Uses neural autoregressive density estimators to model nonlinear variable relationships

Recovers accurate linear and nonlinear causal effects when causal graph is known

Provides reasonable approximations with limited data support if causal graph is accurate

Causal graph topology is critical; unobserved confounding decreases quality

AI generated summary

Estimating Causal Effects with Neural Networks

This paper proposes using neural networks to estimate causal effects, avoiding assumptions about functional relationships between variables. Neural autoregressive density estimators model complex nonlinear interactions. Simulations show the method recovers linear and nonlinear effects when the causal graph is known. It provides reasonable approximations with limited data support if the graph is accurate. Causal topology assumptions are critical; unobserved confounding decreases quality regardless of estimator flexibility.

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