1 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
Philip Boeken, Onno Zoeter, Joris M. Mooij
When predicting a target variable from features , the prediction can be performative: an agent might act on this prediction, affecting the value of that we eve…
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
Philip Boeken, Noud de Kroon, Mathijs de Jong +2
When estimating a regression model, we might have data where some labels are missing, or our data might be biased by a selection mechanism. When the response or selection mechanism…
A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery
Philip A. Boeken, Joris M. Mooij
For a continuous random variable , testing conditional independence is known to be a particularly hard problem. It constitutes a key ingredient of many…