Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach

2019-02-04 · Santtu Tikka, Antti Hyttinen, Juha Karvanen

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and experimental source distributions. The search is enhanced via a heuristic and search space reduction techniques. The approach, called do-search, is provably sound, and it is complete with respect to identifiability problems that have been shown to be completely characterized by do-calculus. When extended with additional rules, the search is capable of handling missing data problems as well. With the versatile search, we are able to approach new problems such as combined transportability and selection bias, or multiple sources of selection bias. We perform a systematic analysis of bivariate missing data problems and study causal inference under case-control design. We also present the R package dosearch that provides an interface for a C++ implementation of the search.

📄 PDF Abstract BibTeX arXiv:1902.01073

Code (0)

등록된 구현이 없습니다.

Tasks

Causal IdentificationCausal InferenceSelection bias

Methods 이 논문이 사용한 방법론

Causal inference Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal…

Exploratory Causal Inference in SAEnce

2025-10-15 · Tommaso Mencattini, Riccardo Cadei, Francesco Locatello arxiv

Randomized Controlled Trials are one of the pillars of science; nevertheless, they rely on hand-crafted hypotheses and expensive analysis. Such constraints prevent causal effect estimation at scale, potentially anchoring…

Causal Inference

Identifying Causal Effects via Context-specific Independence Relations

2020-09-21 · NeurIPS 2019 12 · Santtu Tikka, Antti Hyttinen, Juha Karvanen

Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces co…

Estimating complex causal effects from incomplete observational data

2014-03-05 · Juha Karvanen

Despite the major advances taken in causal modeling, causality is still an unfamiliar topic for many statisticians. In this paper, it is demonstrated from the beginning to the end how causal effects can be estimated from…

Additive models Imputation

Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach

2026-01-01 · Kohei Yoshikawa, Shuichi Kawano arxiv

The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational…

Causal Inference

Federated Causal Inference from Observational Data

2023-08-24 · Thanh Vinh Vo, Young Lee, Tze-Yun Leong

Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints. The presenc…

Causal Inference Federated Learning Gaussian Processes Missing Values+1