# Recovering from Selection Bias in Causal and Statistical Inference

@article{Bareinboim2014RecoveringFS, title={Recovering from Selection Bias in Causal and Statistical Inference}, author={Elias Bareinboim and Jin Tian and Judea Pearl}, journal={Probabilistic and Causal Inference}, year={2014} }

Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. 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…

## 112 Citations

Recovering Causal Effects from Selection Bias

- MathematicsAAAI
- 2015

This paper provides graphical and algorithmic conditions for recov-erability of interventional distributions for when selection and confounding biases are both present and completely characterizes the class of causal effects that are recoverable in Markovian models, and is sufficient for Semi-Markovian Models.

Identification of Causal Effects in the Presence of Selection Bias

- Computer ScienceAAAI
- 2019

This paper investigates the problem of identifiability of causal effects when both confounding and selection biases are simultaneously present, and proposes a new algorithm that subsumes the current state-of-the-art method based on the back-door criterion.

Generalized Adjustment Under Confounding and Selection Biases

- MathematicsAAAI
- 2018

This work generalizes the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias to predict how the population will react when it undergoes a change (intervention), following a new, compulsory decision protocol.

Causal Effect Identification by Adjustment under Confounding and Selection Biases

- MathematicsAAAI
- 2017

This paper derives a sufficient and necessary condition for recovering causal effects using covariate adjustment from an observational distribution collected under preferential selection and presents 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.

Adjustment Criteria for Recovering Causal Effects from Missing Data

- Computer Science, MathematicsECML/PKDD
- 2019

This paper introduces a covariate adjustment formulation for controlling confounding bias in the presence of missing-not-at-random data and develops a necessary and sufficient condition for recovering causal effects using the adjustment.

Recovering from Selection Bias using Marginal Structure in Discrete Models

- Computer Science, MathematicsACI@UAI
- 2015

It is shown that generic identifiability of causal effects is possible in a much wider class of causal models than had previously been known.

Selection Mechanisms and Their Consequences: Understanding and Addressing Selection Bias

- PsychologyCurrent Epidemiology Reports
- 2020

This work used causal directed acyclic graphs to demonstrate the types of bias that can result when selection into a sample is associated with the exposure or outcome of interest, or with both.

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

- Mathematics, Computer Science
- 2019

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal…

Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias

- Mathematics, Computer ScienceUAI
- 2019

We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal…

Instrumental Variables with Treatment-induced Selection: Exact Bias Results

- Economics
- 2020

Instrumental variables (IV) estimation suffers selection bias when the analysis conditions on the treatment. Judea Pearl's early graphical definition of instrumental variables explicitly prohibited…

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