Inferring deterministic causal relations

Daniušis, Povilas; Janzing, Dominik; Mooij, Joris M.; Zscheischler, Jakob; Steudel, Bastian; Zhang, Kun; Schoelkopf, Bernhard

doi:10.48550/arxiv.1203.3475

Cited by 9 publications

(22 citation statements)

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“…We test our method by showing it can turn both the KCDC algorithm of [7] and the IGCI algorithm of [15], which can each only distinguish Figure 1(a) and (b), into algorithms that can distinguish all causal structures in Figure 1. We refer to the modified KCDC and IGCI algorithms output by our method as modKCDC and modIGCI respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The second type of assumption stipulates that the cause P (cause) be independent of the mechanism P (effect|cause). An information geometric approach to measuring such independence has been proposed [6,15] known as the Information Geometric Causal Inference (IGCI) algorithm. This method can only distinguish the two structures in Figure 1(a)-(b).…”

Section: Related Workmentioning

confidence: 99%

“…Here, we focus on directed causal discovery algorithms, D, that decide the causal direction using some quantifiable notion of asymmetry, such as IGCI [15] and KCDC [7], both described in Section II. In general, such algorithms assign a real scalar to each causal direction v D A→B and v D B→A , returning the causal direction with smallest v value.…”

Section: Lemma 1 Any Causal Model Wherein There Is a Directed Arrow B...mentioning

confidence: 99%

“…We apply our method to two state-of-the-art directed causal discovery algorithms, the Information Geometric Causal Inference (IGCI) algorithm of [15] and the Kernel Conditional Deviance for Causal Inference (KCDC) algorithm of [7], and extensively test on synthetic datadetecting latent common causes in additive, multiplicative and complex noise regimes-as well as on real data, where we are able to detect known common causes. Importantly, and in addition to detecting latent common causes with high accuracy, our experiments demonstrate that both modified algorithms do not sacrifice the performance of the original directed algorithm in distinguishing directed causal relations.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging directed causal discovery to detect latent common causes

Lee¹,

Hart²,

Richens³

et al. 2019

Preprint

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The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from uncontrolled data have been proposed. However, most of these deal only with purely directed causal relationships and cannot detect latent common causes. Here, we devise a general method which takes a purely directed causal discovery algorithm and modifies it so that it can also detect latent common causes. The identifiability of the modified algorithm depends on the identifiability of the original, as well as an assumption that the strength of noise be relatively small. We apply our method to two directed causal discovery algorithms, the Information Geometric Causal Inference of (Daniusis et al., 2010) and the Kernel Conditional Deviance for Causal Inference of (Mitrovic, Sejdinovic, & Teh, 2018), and extensively test on synthetic data-detecting latent common causes in additive, multiplicative and complex noise regimes-and on real data, where we are able to detect known common causes. In addition to detecting latent common causes, our experiments demonstrate that both modified algorithms preserve the performance of the original directed algorithm in distinguishing directed causal relations.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Lemma 1 Any Causal Model Wherein There Is a Directed Arrow B...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging directed causal discovery to detect latent common causes

Lee¹,

Hart²,

Richens³

et al. 2019

Preprint

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show abstract

“…This assumption is referred to as causal sufficiency and states that the set of measured variables V include all common causes between pairs of V [39]. We also assume independence between cause and mechanism; that is, if pa i → v i , then the distribution of pa i and the function f mapping pa i → v i are independent of one another [9]. The upshot is f i doesn't change if the distribution of the parents change.…”

Section: Background On Structural Causal Modelsmentioning

confidence: 99%

A Structural Causal Model for MR Images of Multiple Sclerosis

Reinhold¹,

Carass²,

Prince³

2021

Preprint

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Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis (MS). Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like when demographic or disease covariates are changed. These images can be used for modeling disease progression or used for downstream image processing tasks where controlling for confounders is necessary.

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Explainable and Interpretable Models in Computer Vision and Machine Learning

Escalante

Escalera

Guyon

et al. 2018

The Springer Series on Challenges in Machine Learning

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We introduce a new approach to functional causal modeling from observational data, called Causal Generative Neural Networks (CGNN). CGNN leverages the power of neural networks to learn a generative model of the joint distribution of the observed variables, by minimizing the Maximum Mean Discrepancy between generated and observed data. An approximate learning criterion is proposed to scale the computational cost of the approach to linear complexity in the number of observations. The performance of CGNN is studied throughout three experiments. Firstly, CGNN is applied to cause-effect inference, where the task is to identify the best causal hypothesis out of "X → Y " and "Y → X". Secondly, CGNN is applied to the problem of identifying v-structures and conditional independences. Thirdly, CGNN is applied to multivariate functional causal modeling: given a skeleton describing the direct dependences in a set of random variables X = [X 1 , . . . , X d ], CGNN orients the edges in the skeleton to uncover the directed acyclic causal graph describing the causal structure of the random variables. On all three tasks, CGNN is extensively assessed on both artificial and real-world data, comparing favorably to the state-of-the-art. Finally, CGNN is extended to handle the case of confounders, where latent variables are involved in the overall causal model. Keywords: generative neural networks · causal structure discovery · cause-effect pair problem · functional causal models · structural equation models * *

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Inferring deterministic causal relations

Cited by 9 publications

References 0 publications

Leveraging directed causal discovery to detect latent common causes

Leveraging directed causal discovery to detect latent common causes

A Structural Causal Model for MR Images of Multiple Sclerosis

Explainable and Interpretable Models in Computer Vision and Machine Learning

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