From the 1 of 6 linked papers with an AI index.
6 papers
Causal Graphs, Markov Properties and Do-calculus for Stochastic Differential Equations
Philip Boeken, Joris M. Mooij
The paper develops a causal graphical framework for stochastic differential equations, defining conditions for well‑posed observational and interventional distributions and extendi…
Topological Criteria for Hypothesis Testing with Finite-Precision Measurements
Philip Boeken, Eduardo Skapinakis, Konstantin Genin +1
We establish topological necessary and sufficient conditions under which a pair of statistical hypotheses can be consistently distinguished when i.i.d. observations are recorded on…
Are Bayesian networks typically faithful?
Philip Boeken, Patrick Forré, Joris M. Mooij
Faithfulness is a common assumption in causal inference, often motivated by the fact that the faithful parameters of linear Gaussian and discrete Bayesian networks are typical, and…
Testing Partially-Identifiable Causal Queries Using Ternary Tests
Sourbh Bhadane, Joris M. Mooij, Philip Boeken +1
We consider hypothesis testing of binary causal queries using observational data. Since the mapping of causal models to the observational distribution that they induce is not one-t…
Conditional Forecasts and Proper Scoring Rules for Reliable and Accurate Performative Predictions
Philip Boeken, Onno Zoeter, Joris M. Mooij
Performative predictions are forecasts which influence the outcomes they aim to predict, undermining the existence of correct forecasts and standard methods of elicitation and esti…
Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypotheses
Sourbh Bhadane, Joris M. Mooij, Philip Boeken +1
Reasoning about fairness through correlation-based notions is rife with pitfalls. The 1973 University of California, Berkeley graduate school admissions case from Bickel et. al. (1…