Research

Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

Fanny Yang, Zuowen Wang and Christina Heinze-Deml

Under review.
arXiv.

Conditional Variance Penalties and Domain Shift Robustness

Christina Heinze-Deml and Nicolai Meinshausen

Under review.
arXiv.

Invariant Causal Prediction for Nonlinear Models

Christina Heinze-Deml, Jonas Peters and Nicolai Meinshausen

Journal of Causal Inference 6 (2), 2018.
arXiv.

Causal Structure Learning

Christina Heinze-Deml, Marloes H. Maathius and Nicolai Meinshausen

Annual Review of Statistics and Its Application, Volume 5, 2018.
arXiv.

Preserving Privacy Between Features in Distributed Estimation

Christina Heinze-Deml, Brian McWilliams and Nicolai Meinshausen

Stat, Volume 7, Issue 1, 2018.
arXiv.

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

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

Scientific Reports 7, 2017.

Random Projections for Large-Scale Regression

Gian-Andrea Thanei, Christina Heinze, Nicolai Meinshausen

Big and Complex Data Analysis, 2017.
arXiv.

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

CoRe: Conditional Variance Penalties and Domain Shift Robustness

TensorFlow implementation

TensorFlow implementation of 'CoRe' (COnditional Variance REgularization), proposed in "Conditional Variance Penalties and Domain Shift Robustness". The aim is to build classifiers that are robust against specific interventions. These domain-shift interventions are defined in a causal graph, extending the framework of Gong et al (2016). In contrast to Gong et al. we work on a setting where the domain variable itself is latent but we can observe for some instances a so-called identifier variables that indicates, for example, presence of the same person or object across different images. Penalizing the variance of the predictions across instances that share the same class label and identifier leads to robustness against strong domain-shift interventions.
Github.

nonlinearICP

R package

Code for 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016) to nonlinear settings.
Github.

CondIndTests

R package

Code for a variety of nonlinear conditional independence tests: Kernel conditional independence test (Zhang et al., UAI 2011), Residual Prediction test (based on Shah and Buehlmann, arXiv 2015), Invariant environment prediction, Invariant target prediction, Invariant residual distribution test, Invariant conditional quantile prediction (all from Heinze-Deml et al., arXiv:1706.08576).
Github.

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.

Teaching

Causality

Spring 2019

© Christina Heinze-Deml 2019. All rights reserved.