317 citations · 496 across the 18 of their papers we have counts for
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Causal Graphs, Markov Properties and Do-calculus for Stochastic Differential Equations
Philip Boeken, Joris M. Mooij
Stochastic differential equations (SDEs) are widely used to model continuous-time dynamical systems, but graphical causal models for them are not yet well-understood. We consider s…
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…
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…
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…
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…