REJILI, Mohamed (2024) Studying causal discovery in presence of biased data PRE - Research Project, ENSTA.
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Abstract
The goal of this internship is to explore causal discovery in the presence of biased data. To achieve this, we first developed a synthetic data generator based on a causal graph (the ground truth). This generator takes a causal graph and a specified bias level as inputs, and produces synthetic data that maintains the same causal structure as the ground truth while incorporating the given level of bias. Using this synthetic data generator, we create datasets with varying levels of bias for each ground truth. We then apply causal discovery algorithms to these datasets to investigate how the presence and extent of bias affect the accuracy and structure of the discovered causal relationships.
Item Type: | Thesis (PRE - Research Project) |
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Uncontrolled Keywords: | Causal discovery (CD), Bias, Synthetic data generator, Ground truth |
Subjects: | Mathematics and Applications |
ID Code: | 10145 |
Deposited By: | Mohamed REJILI |
Deposited On: | 28 août 2024 11:23 |
Dernière modification: | 02 sept. 2024 16:27 |
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