Practically Effective Adjustment Variable Selection in Causal Inference
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908209852186624 |
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| author | Noda, Atsushi Isozaki, Takashi |
| author_facet | Noda, Atsushi Isozaki, Takashi |
| contents | In the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not always possible to uniquely determine sets of such variables. Moreover, real-world data is almost always limited, which means it may be insufficient for statistical estimation. Therefore, we propose criteria for selecting variables from a list of candidate adjustment variables along with an algorithm to prevent accuracy degradation in causal effect estimation. We initially focus on directed acyclic graphs (DAGs) and then outlines specific steps for applying this method to completed partially directed acyclic graphs (CPDAGs). We also present and prove a theorem on causal effect computation possibility in CPDAGs. Finally, we demonstrate the practical utility of our method using both existing and artificial data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_02701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practically Effective Adjustment Variable Selection in Causal Inference Noda, Atsushi Isozaki, Takashi Machine Learning Artificial Intelligence Data Analysis, Statistics and Probability Methodology In the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not always possible to uniquely determine sets of such variables. Moreover, real-world data is almost always limited, which means it may be insufficient for statistical estimation. Therefore, we propose criteria for selecting variables from a list of candidate adjustment variables along with an algorithm to prevent accuracy degradation in causal effect estimation. We initially focus on directed acyclic graphs (DAGs) and then outlines specific steps for applying this method to completed partially directed acyclic graphs (CPDAGs). We also present and prove a theorem on causal effect computation possibility in CPDAGs. Finally, we demonstrate the practical utility of our method using both existing and artificial data. |
| title | Practically Effective Adjustment Variable Selection in Causal Inference |
| topic | Machine Learning Artificial Intelligence Data Analysis, Statistics and Probability Methodology |
| url | https://arxiv.org/abs/2502.02701 |