Comments (1)
Implemented vanilla causal framework on a built-in doWhy data set. All built in data sets come with structural causal models already encoded. This implementation follows the starter notebook ConfoundingExample on the doWhy website. This starter notebook seemed most relevant as it uses regression and explores data looking for confounders. Documented nuances about the estimate_effect method of CausalModel class from exploring source code.
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Related Issues (20)
- [representation] iS2S: Structure 2 Sequence Code Embeddings HOT 6
- do(code): A Causal Inference Framework to Understand and Explain Source Code Properties
- Integrate pydriller tool
- Integrate Comet into DS4SE
- BPE 32K and 128K SACP
- DS4SE Analysis
- Related work for traceability
- Causality library exploration
- Complete part 1 and 2 of the doWhy tutorial presented at ACM KDD 2018 HOT 1
- Learn how to instantiate the CausalModel class HOT 1
- Learn how to call the identify_effect method of the CausalModel class HOT 1
- Learn how to call the refute_estimate method of the CausalModel class HOT 1
- Look into the new do-sampler feature of doWhy
- Integrate traceability data into the causal prototype established
- Develop potential outcome graphical model with preprocessing as intervention
- Create .gml representation of causal graph for preprocessing intervention =
- Final tasks iS2S HOT 1
- Final Task do(code) HOT 3
- Unconditional Generation Status HOT 1
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