From the 1 of 11 linked papers with an AI index.
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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…
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…
Dynamic Structural Causal Models
Philip Boeken, Joris M. Mooij
We study a specific type of SCM, called a Dynamic Structural Causal Model (DSCM), whose endogenous variables represent functions of time, which is possibly cyclic and allows for la…