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Poster
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Daniel Kumor · Bryant Chen · Elias Bareinboim

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #184
in Probabilistic Methods -- Causal Inference »

One of the most common mistakes made when performing data analysis is attributing causal meaning to regression coefficients. Formally, a causal effect can only be computed if it is identifiable from a combination of observational data and structural knowledge about the domain under investigation (Pearl, 2000, Ch. 5). Building on the literature of instrumental variables (IVs), a plethora of methods has been developed to identify causal effects in linear systems. Almost invariably, however, the most powerful such methods rely on exponential-time procedures. In this paper, we investigate graphical conditions to allow efficient identification in arbitrary linear structural causal models (SCMs). In particular, we develop a method to efficiently find unconditioned instrumental subsets, which are generalizations of IVs that can be used to tame the complexity of many canonical algorithms found in the literature. Further, we prove that determining whether an effect can be identified with TSID (Weihs et al., 2017), a method more powerful than unconditioned instrumental sets and other efficient identification algorithms, is NP-Complete. Finally, building on the idea of flow constraints, we introduce a new and efficient criterion called Instrumental Cutsets (IC), which is able to solve for parameters missed by all other existing polynomial-time algorithms.

Keywords: Causal Inference · Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Daniel Kumor (Purdue University)
Bryant Chen (Brex)
Elias Bareinboim (Columbia University)

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