Causal feature selection with missing data. ACM Transactions on Knowledge Discovery from Data, vol.16, no. 4, Article number 66, 2022. DOI: https://doi.org/10.1145/3488055. X. J. Guo, K. Yu, F. Y. Cao, P. P. Li, H. Wang. Error-aware Markov blanket learning for causal feature...
Ciarán M Lee, and Saurabh Johri. 2020. Improving the accuracy of medical diagnosis with causal ...
Effective decision-making in complex environments requires discerning the relevant from the irrelevant, a challenge that becomes pronounced with large multivariate time-series data. However, existing feature selection algorithms often suffer from complexity and a lack of interpretability, making it difficult...
Causal learning 的一个重要话题是解决out-of-distribtution，要解决它我们可以学习caual feature，causal ...
handling of missing values and masks p-value correction and (bootstrap) confidence interval estimation causal effect class to non-parametrically estimate (conditional) causal effects and also linear mediated causal effects prediction class based on sklearn models including causal feature selection ...
We’re coming out of a hallucinatory period when we thought that the data would be enough. It’s still a concern how few data scientists think about their data collection methods, telemetry, how their analytical decisions (such as removing rows with missing data) introduce statistical bias, and...
Koller D, Sahami M (1996) Toward optimal feature selection. Technical report, Stanford InfoLab Google Scholar Westreich D, Cole SR, Young JG, Palella F, Tien PC, Kingsley L, Gange SJ, Hernán MA (2012) The parametric g-formula to estimate the effect of highly active antiretroviral therapy...
This aspect grants it wide applicability in data sciences. Keywords: Markov blanket; feature selection; causal inference; G-test; information theory; computation reuse1. Introduction Statistical tests of independence are mathematical tools used to determine whether two random variables, recorded in a ...
Zhao, Zhenyu, Yumin Zhang, Totte Harinen, and Mike Yung. "Feature Selection Methods for Uplift Modeling." arXiv preprint arXiv:2005.03447 (2020). Zhao, Zhenyu, and Totte Harinen. "Uplift modeling for multiple treatments with cost optimization." In 2019 IEEE International Conference on Data Sci...
Feature selection: In our case study, the outcome of interest is ‘Delay’, and treatment variables of interest that could be reasonably controlled by the buyers, are the season the order was given in and whether the supplier supplied to multiple warehouses. The latter decision was made due ...