5 papers
Outcome-adapted Automatic Debiased Machine Learning
Asger Waagepetersen, Asbjørn Risom, Niels Richard Hansen +1
Parameters of interest in causal inference, such as treatment or policy effects, can often be expressed as linear functionals of an outcome regression function. Automatic debiased…
Causal Foundations of Collective Agency
Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis Hammond
A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distin…
A Trek Rule for the Lyapunov Equation
Niels Richard Hansen
The Lyapunov equation is a linear matrix equation characterizing the cross-sectional steady-state covariance matrix of a Gaussian Markov process. We show a new version of the trek…
Substitute adjustment via recovery of latent variables
Jeffrey Adams, Niels Richard Hansen
The deconfounder was proposed as a method for estimating causal parameters in a context with multiple causes and unobserved confounding. It is based on recovery of a latent variabl…
Efficient adjustment for complex covariates: Gaining efficiency with DOPE
Alexander Mangulad Christgau, Anton Rask Lundborg, Niels Richard Hansen
Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating…