1.6k citations · 12.4k across the 394 of their papers we have counts for
3 papers · 2 filters
Causal Inference on Time Series using Structural Equation Models
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal inference uses observations to infer the causal structure of the data generating system. We study a class of functional models that we call Time Series Models with Independe…
Identifying confounders using additive noise models
Dominik Janzing, Jonas Peters, Joris Mooij +1
We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder…
Learning from Distributions via Support Measure Machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo +1
This paper presents a kernel-based discriminative learning framework on probability measures. Rather than relying on large collections of vectorial training examples, our framework…