Generalized Adjustment Under Confounding and Selection Biases

@inproceedings{Correa2018GeneralizedAU,
  title={Generalized Adjustment Under Confounding and Selection Biases},
  author={Juan David Correa and Jin Tian and Elias Bareinboim},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2018}
}
Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. [] Key Method We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment.

Figures from this paper

Identification of Causal Effects in the Presence of Selection Bias

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.

Adjustment Criteria for Generalizing Experimental Findings

The assumptions and machinery necessary for using covariate adjustment to correct for the biases generated by both transportability and sampling selection bias are investigated, and experimental data is generalized to infer causal effects in a new domain.

Learning Adjustment Sets from Observational and Limited Experimental Data

This work introduces a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects and can sometimes make additional inferences when compared to state-of-the-art methods for combining experimental and observational data.

Local Constraint-Based Causal Discovery under Selection Bias

A finite-sample scoring rule for Y-Structures is introduced that is shown to successfully predict causal relations in simulation experiments that include selection mechanisms, and it is shown that a Y-Structure variant performs well across different datasets, potentially circumventing spurious correlations due to selection bias.

Adjustment Criteria for Recovering Causal Effects from Missing Data

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.

Causal Inference and Data-Fusion in Econometrics

Recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality.

Generalizing a causal effect: sensitivity analysis and missing covariates

While a randomized controlled trial (RCT) readily measures the average treatment effect (ATE), this measure may need to be shifted to generalize to a different population. Standard estimators of the

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias

An algorithm to estimate causal effects from multiple data sources, where the ATE may be identifiable only in some datasets but not others, and a construction of control variate by taking the diflerence of the conditional odds ratio estimates from the two datasets is proposed.

Causal effect on a target population: A sensitivity analysis to handle missing covariates

Abstract Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is

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

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

References

SHOWING 1-10 OF 38 REFERENCES

Causal Effect Identification by Adjustment under Confounding and Selection Biases

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.

Recovering from Selection Bias in Causal and Statistical Inference

This paper provides complete graphical and algorithmic conditions for recovering conditional probabilities from selection biased data and also provides graphical conditions for recoverability when unbiased data is available over a subset of the variables.

Controlling Selection Bias in Causal Inference

This paper highlights several graphical and algebraic methods capable of mitigating and sometimes eliminating selection bias and generalize and improve previously reported results, and identify the type of knowledge that need to be available for reasoning in the presence of selection bias.

On the Validity of Covariate Adjustment for Estimating Causal Effects

This paper gives a complete graphical criterion for covariate adjustment, which is term the adjustment criterion, and derive some interesting corollaries of the completeness of this criterion.

On recovering a population covariance matrix in the presence of selection bias

This paper considers the problem of using observational data in the presence of selection bias to identify causal effects in the framework of linear structural equation models. We propose a criterion

Causal Inference by Surrogate Experiments: z-Identifiability

The problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation is addressed and a graphical necessary and sufficient condition for z-identifiability for arbitrary sets X,Z, and Y is provided.

Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score,

Causal inference and the data-fusion problem

This work addresses the problem of data fusion—piecing together multiple datasets collected under heterogeneous conditions to obtain valid answers to queries of interest and presents a general, nonparametric framework for handling these biases.

A general identification condition for causal effects

The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and a powerful sufficient criterion on any set of variables.

Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models

A necessary and sufficient graphical condition is provided for the cases when the causal effect of an arbitrary set of variables on another arbitrary set can be determined uniquely from the available information, as well as an algorithm which computes the effect whenever this condition holds.