Causal Inference with Deep Causal Graphs

• URL: http://arxiv.org/abs/2006.08380v1
• Date: Mon, 15 Jun 2020 13:03:33 GMT
• Title: Causal Inference with Deep Causal Graphs
• Authors: \'Alvaro Parafita and Jordi Vitri\`a
• Abstract summary: Parametric causal modelling techniques rarely provide functionality for counterfactual estimation. Deep Causal Graphs is an abstract specification of the required functionality for a neural network to model causal distributions. We demonstrate its expressive power in modelling complex interactions and showcase applications to machine learning explainability and fairness.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parametric causal modelling techniques rarely provide functionality for counterfactual estimation, often at the expense of modelling complexity. Since causal estimations depend on the family of functions used to model the data, simplistic models could entail imprecise characterizations of the generative mechanism, and, consequently, unreliable results. This limits their applicability to real-life datasets, with non-linear relationships and high interaction between variables. We propose Deep Causal Graphs, an abstract specification of the required functionality for a neural network to model causal distributions, and provide a model that satisfies this contract: Normalizing Causal Flows. We demonstrate its expressive power in modelling complex interactions and showcase applications of the method to machine learning explainability and fairness, using true causal counterfactuals.

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