Causal Effect Identification by Adjustment under Confounding and Selection Biases
Authors: Juan Correa, Elias Bareinboim
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce a generalized version of covariate adjustment that simultaneously controls for both confounding and selection biases. We first derive a sufficient and necessary condition for recovering causal effects using covariate adjustment from an observational distribution collected under preferential selection. We then relax this setting to consider cases when additional, unbiased measurements over a set of covariates are available for use (e.g., the age and gender distribution obtained from census data). Finally, we present a complete algorithm with polynomial delay to find all sets of admissible covariates for adjustment when confounding and selection biases are simultaneously present and unbiased data is available. Specifically, we solved the following problems: 1. Identification and recoverability without external data: The data is collected under selection bias, P(v | S=1), when does a set of covariates Z allow P(y | do(x)) to be estimated by adjusting for Z? 2. Identification and recoverability with external data: The data is collected under selection bias P(v | S=1) and unbiased samples of P(t), T V, are available. When does a set of covariates Z T license the estimation of P(y | do(x)) by adjusting for Z? 3. Finding admissible adjustment sets with external data: How can we list all admissible sets Z capable of identifying and recovering P(y | do(x)), for Z T V? |
| Researcher Affiliation | Academia | Juan D. Correa Purdue University correagr@purdue.edu Elias Bareinboim Purdue University eb@purdue.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks found. The paper refers to the LISTSEP procedure from an external source but does not provide its implementation details. |
| Open Source Code | No | No statement regarding the release of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific public dataset for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned that are required to replicate the theoretical derivations or proposed algorithm. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters. |