Estimation of causal effects using linear non-Gaussian causal models with hidden variables

Hoyer, Patrik O.; Shimizu, Shohei; Kerminen, Antti; Palviainen, Markus

doi:10.1016/j.ijar.2008.02.006

Cited by 137 publications

(147 citation statements)

References 7 publications

Supporting

Mentioning

146

Contrasting

Unclassified

Order By: Relevance

“…Algorithms which learn latent variable LiNGAM models (Hoyer et al, 2008; Kawahara et al, 2010; Entner and Hoyer, 2010; Tashiro et al, 2014) allow for the possibility of unmeasured variables. These algorithms exploit assumptions about the causal structure (assumed to be structural equation models which are acyclic, linear, and which have non-Gaussian error terms) to estimate graphical structure and some estimate causal strength parameters simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Malinsky

Spirtes

2017

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent confounders. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to adjust for) to estimate a set of possible causal effects. Our approach is based on the IDA procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no latent confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm in simulation experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Malinsky

Spirtes

2017

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

show abstract

“…x?y) tend to be small even for larger confounder effects 4 (see also Hoyer, Shimizu, Kerminen, & Palviainen, 2008), implying that our results may still be valid. However, results may additionally be influenced by specific properties of the items.…”

Section: Fs)mentioning

confidence: 56%

Direction of dependence in measurement error models

Wiedermann

Merkle

Eye

2017

Brit J Math & Statis

View full text Add to dashboard Cite

Methods to determine the direction of a regression line, that is, to determine the direction of dependence in reversible linear regression models (e.g., x?y vs. y?x), have experienced rapid development within the last decade. However, previous research largely rested on the assumption that the true predictor is measured without measurement error. The present paper extends the direction dependence principle to measurement error models. First, we discuss asymmetric representations of the reliability coefficient in terms of higher moments of variables and the attenuation of skewness and excess kurtosis due to measurement error. Second, we identify conditions where direction dependence decisions are biased due to measurement error and suggest method of moments (MOM) estimation as a remedy. Third, we address data situations in which the true outcome exhibits both regression and measurement error, and propose a sensitivity analysis approach to determining the robustness of direction dependence decisions against unreliably measured outcomes. Monte Carlo simulations were performed to assess the performance of MOM-based direction dependence measures and their robustness to violated measurement error assumptions (i.e., non-independence and non-normality). An empirical example from subjective wellbeing research is presented. The plausibility of model assumptions and links to modern causal inference methods for observational data are discussed.

show abstract

“…Accordingly, one way to estimate the causal structure from observed data based on the FCM is to first fit the model on given data and then test for independence between the estimated noise term and the hypothetical cause. So far functional causal discovery has been mainly concerned with cases without confounders or feedbacks, with several exceptions [7,8]. …”

Section: Learning Causal Relationsmentioning

confidence: 99%

Learning causality and causality-related learning: some recent progress

Zhang

Schoelkopf

Spirtes

et al. 2017

National Science Review

View full text Add to dashboard Cite

Large language models (LLMs) have shown various ability on natural language processing, including problems about causality. It is not intuitive for LLMs to command causality, since pretrained models usually work on statistical associations, and do not focus on causes and effects in sentences. So that probing internal manipulation of causality is necessary for LLMs. This paper proposes a novel approach to probe causality manipulation hierarchically, by providing different shortcuts to models and observe behaviors. We exploit retrieval augmented generation (RAG) and in-context learning (ICL) for models on a designed causality classification task. We conduct experiments on mainstream LLMs, including GPT-4 and some smaller and domain-specific models. Our results suggest that LLMs can detect entities related to causality and recognize direct causal relationships. However, LLMs lack specialized cognition for causality, merely treating them as part of the global semantic of the sentence. 1 * Equally Contribution. 1 Our code and implementation are available at https://github.com/TongjiNLP/llm-causality-probing.

show abstract

Estimation of causal effects using linear non-Gaussian causal models with hidden variables

Cited by 137 publications

References 7 publications

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Direction of dependence in measurement error models

Learning causality and causality-related learning: some recent progress

Contact Info

Product

Resources

About