Research

Predicting the effect of interventions using invariance principles for nonlinear models

Christina Heinze-Deml, Jonas Peters and Nicolai Meinshausen

Preprint.

Preserving Differential Privacy Between Features in Distributed Estimation

Christina Heinze-Deml, Brian McWilliams and Nicolai Meinshausen

NIPS 2016 Workshop on Private Multi-Party Machine Learning.

Predicting causal relationships from biological data: Comparison of computational methods for automated causal discovery applied to mass cytometry data of human immune cells

Sofia Triantafillou, Vincenzo Lagani, Christina Heinze-Deml, Angelika Schmidt, Jesper Tegner and Ioannis Tsamardinos

Preprint.

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections

Christina Heinze, Brian McWilliams and Nicolai Meinshausen

AISTATS 2016.
arXiv. Spark package.

backShift: Learning causal cyclic graphs from unknown shift interventions

Dominik Rothenhaeusler, Christina Heinze, Jonas Peters and Nicolai Meinshausen

Advances in Neural Information Processing Systems (NIPS) 28, 2015.
arXiv. R package.

LOCO: Distributing Ridge Regression with Random Projections

Christina Heinze, Brian McWilliams, Nicolai Meinshausen and Gabriel Krummenacher

NIPS Workshop on Distributed Machine Learning and Matrix Computations 2014.
arXiv. Spark package.

Software

CompareCausalNetworks: Interface to Diverse Estimation Methods of Causal Networks

R package

Unified interface for the estimation of causal networks, including the methods 'backShift', 'bivariateANM' (bivariate additive noise model), 'bivariateCAM' (bivariate causal additive model), 'CAM' (causal additive model), 'hiddenICP' (invariant causal prediction with hidden variables), 'ICP' (invariant causal prediction), 'GES' (greedy equivalence search), 'GIES' (greedy interventional equivalence search), 'LINGAM', 'PC' (PC Algorithm), 'RFCI' (really fast causal inference) and regression.
CRAN. Github.

backShift: Learning causal cyclic graphs from unknown shift interventions

R package

Code for 'backShift', an algorithm to estimate the connectivity matrix of a directed (possibly cyclic) graph with hidden variables.
CRAN. Github.

LOCOlib

Spark package

LOCOlib implements the LOCO and DUAL-LOCO algorithms for distributed statistical estimation.
Github.

© Christina Heinze-Deml 2017. All rights reserved.