Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
📰 ArXiv cs.AI
Learn to estimate average causal effects without relying on pretreatment and causal sufficiency assumptions using local covariate selection methods
Action Steps
- Apply local covariate selection methods to identify relevant variables for causal effect estimation
- Use techniques such as regularization or recursive feature elimination to select covariates
- Estimate the average causal effect using the selected covariates and evaluate the results
- Compare the performance of different covariate selection methods to choose the best approach
- Implement the selected method in a real-world dataset to demonstrate its effectiveness
Who Needs to Know This
Data scientists and researchers working on causal inference projects can benefit from this approach to improve the accuracy of their estimates without requiring strong assumptions
Key Insight
💡 Local covariate selection can be used to estimate average causal effects without relying on pretreatment and causal sufficiency assumptions
Share This
📊 Estimate average causal effects without strong assumptions using local covariate selection! 🚀
Full Article
Title: Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
Abstract:
arXiv:2605.21548v1 Announce Type: cross Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency - where observed variables share no latent confounders - or the pretreatment assumption, which limits covariates to those unaffected by the treatment or outcome. These requirements are often unrealistic in pract
Abstract:
arXiv:2605.21548v1 Announce Type: cross Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency - where observed variables share no latent confounders - or the pretreatment assumption, which limits covariates to those unaffected by the treatment or outcome. These requirements are often unrealistic in pract
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