Modeling Latent Selection with Structural Causal Models
Selection bias is ubiquitous in real-world data, and can lead to misleading results if not dealt with properly. We introduce a conditioning operation on Structural Causal Models (SCMs) to model latent selection from a causal perspective. We show that the conditioning operation transforms an SCM with the presence of an explicit latent selection mechanism into an SCM without such selection mechanism, which partially encodes the causal semantics of the selected subpopulation according to the original SCM. Furthermore, we show that this conditioning operation preserves the simplicity, acyclicity, and linearity of SCMs, and commutes with marginalization. Thanks to these properties, combined with marginalization and intervention, the conditioning operation offers a valuable tool for conducting causal reasoning tasks within causal models where latent details have been abstracted away. We demonstrate by example how classical results of causal inference can be generalized to include selection bias and how the conditioning operation helps with modeling of real-world problems.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceSelection biasMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
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 models that allow for…
Selection biasA Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variab…
Causal DiscoveryCausal InferenceSelection biasConstraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We …
Causal DiscoveryDetecting and Identifying Selection Structure in Sequential Data
We argue that the selective inclusion of data points based on latent objectives is common in practical situations, such as music sequences. Since this selection process often distorts statistical analysis, previous work …
Inductive BiasFoundations of Structural Causal Models with Cycles and Latent Variables
Structural causal models (SCMs), also known as (nonparametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive SEMs, form a well-studi…
counterfactual