Identifying Causal Effects Under Functional Dependencies
We study the identification of causal effects, motivated by two improvements to identifiability which can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Second, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure which removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
TSLiNGAM: DirectLiNGAM under heavy tails
One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. Possible…
Causal DiscoveryIdentifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
Understanding causal relations in dynamic systems is essential in epidemiology. While causal inference methods have been extensively studied, they often rely on fully specified causal graphs, which may not always be avai…
Causal InferenceEpidemiologyEstimating Causal Effects with the Neural Autoregressive Density Estimator
Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, tha…
Causal InferenceOn the Granularity of Causal Effect Identifiability
The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the identifiability of state-based causal effects: how an intervention on a particu…
Identifying Causal Effects Using a Single Proxy Variable
Unobserved confounding is a key challenge when estimating causal effects from a treatment on an outcome in scientific applications. In this work, we assume that we observe a single, potentially multi-dimensional proxy va…