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...
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...
对于所有环境来说，模型output和它的causal variable (即causal feature) 的关系是所有关系中唯一不会改变...
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 ...
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 ...
(Publication)Uplift Modeling for Multiple Treatments with Cost Optimizationat2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (Publication)Feature Selection Methods for Uplift Modeling Citation To cite CausalML in publications, you can refer to the following sources: ...
The first one is, of course, related to Bayesian post-selection inference (which I first asked you about five years ago. Back then, you admitted that Bayesians are not immune to overfitting when using the same data to modify/extend a model and to predict with it. Recently, you even ...
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...
First, you would need to partition your data into segments defined by the feature X . This would be fine if you had very few discrete features. But what if there are many of them, with some being continuous? For example, let’s say you know the bank used 10 variables, each with 3 ...
selection repeats with replacement until at least M observations are included in the bootstrap sample. The same features can be randomly selected multiple times and can be included as neighbors multiple times. Using random neighborhoods rather than completely random selection helps correct for ...