48 citations · 188 across the 17 of their papers we have counts for
5 papers · 1 filter
Approximate Causal Abstraction
Sander Beckers, Frederick Eberhardt, Joseph Y. Halpern
Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of obse…
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Antti Hyttinen, Sergey Plis, Matti Järvisalo +2
This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system…
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
Antti Hyttinen, Patrik O. Hoyer, Frederick Eberhardt +1
We present a very general approach to learning the structure of causal models based on d-separation constraints, obtained from any given set of overlapping passive observational or…
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables
Frederick Eberhardt, Clark Glymour, Richard Scheines
We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N) + 1 experiments are sufficient and in the worst…
Almost Optimal Intervention Sets for Causal Discovery
Frederick Eberhardt
We conjecture that the worst case number of experiments necessary and sufficient to discover a causal graph uniquely given its observational Markov equivalence class can be specifi…