The covariance of causal effect estimators for binary v-structures
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917959827456000 |
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| author | Kuipers, Jack Moffa, Giusi |
| author_facet | Kuipers, Jack Moffa, Giusi |
| contents | Previously [Journal of Causal Inference, 10, 90-105 (2022)], we computed the variance of two estimators of causal effects for a v-structure of binary variables. Here we show that a linear combination of these estimators has lower variance than either. Furthermore, we show that this holds also when the treatment variable is block randomised with a predefined number receiving treatment, with analogous results to when it is sampled randomly. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_14242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The covariance of causal effect estimators for binary v-structures Kuipers, Jack Moffa, Giusi Statistics Theory Previously [Journal of Causal Inference, 10, 90-105 (2022)], we computed the variance of two estimators of causal effects for a v-structure of binary variables. Here we show that a linear combination of these estimators has lower variance than either. Furthermore, we show that this holds also when the treatment variable is block randomised with a predefined number receiving treatment, with analogous results to when it is sampled randomly. |
| title | The covariance of causal effect estimators for binary v-structures |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2503.14242 |