Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience

Causal inference based on counterfactuals

Illustrating bias due to conditioning on a collider

Towards Clarifying the Theory of the Deconfounder

On Geometry of Information Flow for Causal Inference

Directed Acyclic Graphs and causal thinking in clinical risk prediction modeling

Assessing bias in case-control studies.

The Seven Tools of Causal Inference, with Reflections on Machine Learning

Generalizing Experimental Results by Leveraging Knowledge of Mechanisms

Note on ‘‘Generalizability of Study Results‘‘

THE LIMITATIONS OF OPAQUE LEARNING MACHINES

Sensitivity Analysis of Linear Structural Causal Models

The Environment and Disease: Association or Causation?

A Crash Course in Good and Bad Controls

Confounding and Collapsibility in Causal Inference

Causal inference-so much more than statistics