Philipp Geiger's web page

Publications

Selection

1. Geiger, P. (2017). Causal models for decision making via integrative inference. PhD thesis. [Download: publication, slides.]
2. Geiger, P., Zhang, K., Gong, M., Janzing, D., & Schölkopf, B. (2015). Causal inference by identification of vector autoregressive processes with hidden components. In Proceedings of the 32th International Conference on Machine Learning (ICML 2015). [Download: publication, slides.]
3. Geiger, P., Janzing, D., & Schölkopf, B. (2014). Estimating causal effects by bounding confounding. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014). [Download: publication, supplement, slides.]

Peer-reviewed publications

1. Geiger, P., Zhang, K., Gong, M., Janzing, D., & Schölkopf, B. (2015). Causal inference by identification of vector autoregressive processes with hidden components. In Proceedings of the 32nd International Conference on Machine Learning (ICML). [Download: publication, slides.]
2. Gong, M., Zhang, K., Schoelkopf, B., Tao, D., & Geiger, P. (2015). Discovering temporal causal relations from subsampled data. In Proceedings of the 32nd International Conference on Machine Learning (ICML). [Download: publication.]
3. Geiger, P., Janzing, D., & Schölkopf, B. (2014). Estimating causal effects by bounding confounding. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [Download: publication, supplement, slides.]

Preprints

1. Geiger, P., Winkelmann, J., Proissl, C., Besserve, M., & Schölkopf, B. (2018). Coordination via predictive assistants from a game-theoretic view. ArXiv Preprint ArXiv:1803.06247. [Download: publication.]
2. Geiger, P., Carata, L., & Schoelkopf, B. (2016). Causal inference for cloud computing. ArXiv Preprint ArXiv:1603.01581. [Download: publication.]
3. Geiger, P., Hofmann, K., & Schölkopf, B. (2016). Experimental and causal view on information integration in autonomous agents. ArXiv Preprint ArXiv:1606.04250. [Download: publication, slides.]

Theses

1. Geiger, P. (2017). Causal models for decision making via integrative inference. PhD thesis. [Download: publication, slides.]
2. Geiger, P. (2012). Mutual information and Gödel incompleteness. Diploma thesis. [Download: publication.]

Notes

1. Geiger, P. (2016). Notes on socio-economic transparency mechanisms. ArXiv Preprint ArXiv:1606.04703. [Download: publication.]

Blog

I'm trying out this blog as a means to express preliminary, speculative thoughts in a straightforward way. This straightforward way may induce some inaccuracies which I kindly ask the reader to excuse.

• Implementing time series multi-step ahead forecasts using recurrent neural networks in TensorFlow:
  

  Recently I started to use recursive neural networks (RNNs) in TensorFlow (TF) for time series forecasting. Specifically, I’d like to perform multistep ahead forecasts and I was wondering how to do this (1) with RNNs in general and (2) in TF in particular. Here I summarize my insights. In particular I give a short overview over some available approaches. Furthermore, I provide code on GitHub which evaluates two simple approaches on real data.

  
  [Read more. Posted on 2018-05-26.]
• Machine learning, automated reasoning and argumentation:
  

  This week I attended ECAI (European Conference on Artificial Intelligence) 2016 and I will summarize some of my impressions and further thoughts here.

  
  [Read more. Posted on 2016-09-02. Updated on 2018-05-26.]

Various slides and notes

Coming soon.

Software

Further programs are available on GitHub.

• CLH: CLH stands for "Causal inference algorithm for Linear processes with Hidden confounders". This is a package containing a Matlab implementation of Algorithm 1 of our paper "Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components", called CLH_NV. The package also contains an example how to run the algorithm on simulated data. Please keep in mind that this is a very preliminary implementation for test purposes. [Download: CLH.zip.]