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20092026
most citedCausal Inference in the Presence of Latent Variables and Selection Bias

189 citations · 846 across the 31 of their papers we have counts for

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Showing 2013Show all

5 papers · 1 filter

cs.AI2013★ 10 cited

Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models

Peter L. Spirtes

The Trek Separation Theorem (Sullivant et al. 2010) states necessary and sufficient conditions for a linear directed acyclic graphical model to entail for all possible values of it…

cs.AI2013★ 1 cited

Detecting Causal Relations in the Presence of Unmeasured Variables

Peter L. Spirtes

The presence of latent variables can greatly complicate inferences about causal relations between measured variables from statistical data. In many cases, the presence of latent va…

cs.AI2013★ 189 cited

Causal Inference in the Presence of Latent Variables and Selection Bias

Peter L. Spirtes, Christopher Meek, Thomas S. Richardson

We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias…

cs.AI2013★ 143 cited

Directed Cyclic Graphical Representations of Feedback Models

Peter L. Spirtes

The use of directed acyclic graphs (DAGs) to represent conditional independence relations among random variables has proved fruitful in a variety of ways. Recursive structural equa…

stat.ME2013★ 5 cited

Semi-Instrumental Variables: A Test for Instrument Admissibility

Tianjiao Chu, Richard Scheines, Peter L. Spirtes

In a causal graphical model, an instrument for a variable X and its effect Y is a random variable that is a cause of X and independent of all the causes of Y except X. (Pearl (1995…