Recovering from Selection Bias in Causal and Statistical Inference
Authors: Elias Bareinboim, Jin Tian, Judea Pearl
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 eb@cs.ucla.edu Jin Tian Department of Computer Science Iowa State University Ames, IA. 50011 jtian@iastate.edu Judea Pearl Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles Los Angeles, CA. 90095 judea@cs.ucla.edu |
| Pseudocode | Yes | For W, Z M, consider the problem of recovering P(w|z) from P(t) and P(m|S = 1), and define 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. |