189 citations · 846 across the 31 of their papers we have counts for
7 papers · 1 filter
Towards a holistic understanding of Selection Bias for Causal Effect Identification
Yiwen Qiu, Filip Kovačević, Shimeng Huang +2
Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher soc…
Causal discovery for observational sciences using supervised machine learning
Anne Helby Petersen, Joseph Ramsey, Claus Thorn Ekstrøm +1
Causal inference can estimate causal effects, but unless data are collected experimentally, statistical analyses must rely on pre-specified causal models. Causal discovery algorith…
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Kun Zhang, Mingming Gong, Joseph Ramsey +3
Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fi…
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before…
A Uniformly Consistent Estimator of Causal Effects under the -Triangle-Faithfulness Assumption
Peter Spirtes, Jiji Zhang
Spirtes, Glymour and Scheines [Causation, Prediction, and Search (1993) Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure…
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…