Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Recovering from Selection Bias in Causal and Statistical Inference
Authors: Elias Bareinboim, Jin Tian, Judea Pearl
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data. We also provide graphical conditions for recoverability when unbiased data is available over a subset of the variables. Finally, we provide a graphical condition that generalizes the backdoor criterion and serves to recover causal effects when the data is collected under preferential selection. |
| Researcher Affiliation | Academia | Elias Bareinboim Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles Los Angeles, CA. 90095 EMAIL Jin Tian Department of Computer Science Iowa State University Ames, IA. 50011 EMAIL Judea Pearl Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles Los Angeles, CA. 90095 EMAIL |
| Pseudocode | Yes | For W, Z M, consider the problem of recovering P(w|z) from P(t) and P(m|S = 1), and deο¬ne procedure RC(w, z) as follows: 1. If W Z T, then P(w|z) is s-recoverable. 2. If (S W|Z), then P(w | z) is s-recoverable as P(w | z) = P(w | z, S = 1). 3. For minimal C M such that (S W|(Z C)), P(w|z) = P c P(w|z, c, S = 1)P(c|z). If C Z T, then P(w|z) is s-recoverable. Otherwise, call RC(c, z). 4. For some W W, P(w|z) = P(w |w \ w , z)P(w \ w , z). Call RC(w , {w \ w } z) and RC(w \ w , z)). 5. Exit with FAIL (to s-recover P(w|z)) if for a singleton W, none of the above operations are applicable. |
| Open Source Code | No | No explicit statement or link for open-source code for the described methodology was found. |
| Open Datasets | No | This is a theoretical paper and does not involve training on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve data validation. |
| Hardware Specification | No | This is a theoretical paper; no hardware specifications are mentioned for experiments. |
| Software Dependencies | No | This is a theoretical paper; no software dependencies with version numbers are specified. |
| Experiment Setup | No | This is a theoretical paper; no experimental setup details like hyperparameters are specified. |