Fredrik D. Johansson, PhD

Assistant professor of Computer Science & Engineering
DSAI.se at Chalmers University of Technology, Sweden
Machine Learning for Causal Inference & Healthare

Research

My research interests are primarily focused on causal inference in machine learning with applications to healthcare. I have also been working on graph-related methods and problems, including graph kernels and probabilistic graphical models.

I am an affilate with the Clinical Machine Learning group at CSAIL & IMES, Massachusetts Institute of Technology working with Professor David Sontag.

Here is my CV (Jan '20).

Contact

You can contact me by email at fredrik.johansson@chalmers.se or ml@fredjo.com.

Publications

Here is my Google Scholar profile.

Pre-prints

Journal articles

Peer-reviewed conference publications

Technical reports

Workshop papers

PhD Thesis

Software & Data

Counterfactual Regression is a package for treatment effect estimation in Python.

IHDP-100 (train), IHDP-100 (test) are 100 realizations of the IHDP dataset as used in Shalit, J, Sontag, ICML, 2017. Variables in the .npz x, t, yf, ycf, mu0, mu1 are covariates, treatment, factual outcome, counterfactual outcome, and noiseless potential outcomes respectively.

Jobs-Binary (train), Jobs-Binary (test) is the preprocessed Jobs dataset, following the same format as IHDP, as used in Shalit, J, Sontag, ICML, 2017. Variables in the .npz x, t, yf, e are covariates, treatment, factual outcome and indicator for original randomized sample respectively.

IHDP-1000 (train), IHDP-1000 (test) are 1000 realizations of the IHDP dataset as used in Shalit, J, Sontag, ICML, 2017.

News, is a dataset for treatment effect estimation as used in J, Shalit, Sontag, ICML, 2016.

Matching Kernels is a package of graph kernels for MATLAB.

Lovasz-theta Kernel is a package of graph kernels for MATLAB.

DLOREAN is a software for inferring multi-way dynamic networks from node data.

Entity Graphs is a collection of graphs of entity interactions built from data collected by Recorded Future.