kailiang-zhong/DESCN
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
This project helps data scientists, statisticians, and researchers understand the causal impact of an intervention, often called a 'treatment effect.' It takes datasets with information on interventions (like a new marketing campaign or drug) and outcomes, then predicts what would have happened if individuals had received a different treatment. The output helps identify the true impact of specific actions.
108 stars. No commits in the last 6 months.
Use this if you need to precisely measure the causal effect of a specific action or treatment in situations where you can't run a perfect A/B test.
Not ideal if you are looking for general predictive modeling or correlation analysis, rather than a specific causal impact of a binary treatment.
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BSD-2-Clause
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Last pushed
Dec 27, 2023
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