Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs
Causal Queries are usually estimated by means of an estimand, a formula consisting of observational terms that can be computed using passive data. Each query results in a different formula, which makes estimand-based methods extremely ad-hoc. In this work, we propose an estimand-agnostic framework c...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9815055/ |
| _version_ | 1828763954887786496 |
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| author | Alvaro Parafita Jordi Vitria |
| author_facet | Alvaro Parafita Jordi Vitria |
| author_sort | Alvaro Parafita |
| collection | DOAJ |
| description | Causal Queries are usually estimated by means of an estimand, a formula consisting of observational terms that can be computed using passive data. Each query results in a different formula, which makes estimand-based methods extremely ad-hoc. In this work, we propose an estimand-agnostic framework capable of computing any identifiable causal query on an arbitrary Causal Graph (even in the presence of latent confounders) with only one general model. We provide multiple implementations of this general framework that leverage the expressive power of Neural Networks and Normalizing Flows to model complex distributions, and we derive estimation procedures for all kinds of observational, interventional and counterfactual queries, valid for any kind of graph for which the query is identifiable. Finally, we test our techniques in a modelling setting and an estimation benchmark to show how, despite being a query-agnostic framework, it can compete with query-specific models. Our proposal includes an open-source library that allows easy application and extension of our techniques for researchers and practitioners alike. |
| first_indexed | 2024-12-11T02:11:54Z |
| format | Article |
| id | doaj.art-4247e21a3a78469e9f13767525487601 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T02:11:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4247e21a3a78469e9f137675254876012022-12-22T01:24:16ZengIEEEIEEE Access2169-35362022-01-0110713707138610.1109/ACCESS.2022.31883959815055Estimand-Agnostic Causal Query Estimation With Deep Causal GraphsAlvaro Parafita0https://orcid.org/0000-0002-5446-3612Jordi Vitria1https://orcid.org/0000-0003-1484-539XDepartament de Matemàtica i Informàtica, Universitat de Barcelona, Barcelona, SpainDepartament de Matemàtica i Informàtica, Universitat de Barcelona, Barcelona, SpainCausal Queries are usually estimated by means of an estimand, a formula consisting of observational terms that can be computed using passive data. Each query results in a different formula, which makes estimand-based methods extremely ad-hoc. In this work, we propose an estimand-agnostic framework capable of computing any identifiable causal query on an arbitrary Causal Graph (even in the presence of latent confounders) with only one general model. We provide multiple implementations of this general framework that leverage the expressive power of Neural Networks and Normalizing Flows to model complex distributions, and we derive estimation procedures for all kinds of observational, interventional and counterfactual queries, valid for any kind of graph for which the query is identifiable. Finally, we test our techniques in a modelling setting and an estimation benchmark to show how, despite being a query-agnostic framework, it can compete with query-specific models. Our proposal includes an open-source library that allows easy application and extension of our techniques for researchers and practitioners alike.https://ieeexplore.ieee.org/document/9815055/Causalitystructural causal modelcausal query estimationcounterfactuals |
| spellingShingle | Alvaro Parafita Jordi Vitria Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs IEEE Access Causality structural causal model causal query estimation counterfactuals |
| title | Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs |
| title_full | Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs |
| title_fullStr | Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs |
| title_full_unstemmed | Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs |
| title_short | Estimand-Agnostic Causal Query Estimation With Deep Causal Graphs |
| title_sort | estimand agnostic causal query estimation with deep causal graphs |
| topic | Causality structural causal model causal query estimation counterfactuals |
| url | https://ieeexplore.ieee.org/document/9815055/ |
| work_keys_str_mv | AT alvaroparafita estimandagnosticcausalqueryestimationwithdeepcausalgraphs AT jordivitria estimandagnosticcausalqueryestimationwithdeepcausalgraphs |