Copenhagen Causality Lab (CoCaLa)
Causality is a fundamental concept in science, and our research is centered around this concept and how it relates to statistical modeling and data analysis. Some main aspects of our research are outlined below.
Graphical models
Independence statements in probability models can be studied via graphs, and models based on e.g. directed acyclic graphs are used widely as causal models. Graph theory in relation to causal modeling is a core activity of our research. The goal is a deep understanding of how graphical models in a wide sense can be used to encode independence, how they behave as mathematical objects and as statistical models, and how to compute efficiently with graphical models for practical data analysis.
Dynamical models
There is a flow from cause to effect along the arrow of time. This is a salient assumption of a causal model, which can be made explicit for models of discrete-time dynamical systems (time series models) using conventional graphical models. For event process models and other continuous-time dynamic stochastic models new developments are required. One goal is to expand the applicability of graphical models beyond the discrete-time framework. Another is to develop practical methods for causal modeling of dynamic stochastic systems.
Structure learning
Data driven selection among multiple causal models is a main practical challenge. We investigate constraint and score based algorithms for graphical model selection. A particular focus is on models with latent variable structures to handle incomplete observations from a causal model. Specific developments include non-parametric conditional independence tests, model scoring, and invariant prediction.
Open Positions
PhD and postdoc positions within CoCaLa will be announced via the departments regular calls, see Vacant positions.
The next PhD call will have a deadline in April. The annual postdoc call will open in October with a deadline in mid-November.
Positions related to specific projects within CoCaLa are described in the general calls, but you are welcome to contact us if you have questions regarding open positions.
Funding
We are funded by
- VILLUM FONDEN Young Investigator, Causal Learning in Real World Applications, 2018-2023, PI: Jonas Peters
- Carlsberg Foundation, Learning by doing: How to adapt to changing environments, 2018-2021, PI: Jonas Peters
- VILLUM FONDEN, Causal inference with incomplete data, 2016-2020,
PI: Niels Richard Hansen - The Dynamical Systems Interdisciplinary Network, 2013-2017,
PI: Susanne Ditlevsen
Present guests
Future guests
Past guests
Members
Permanent
Mathias Drton
Niels Richard Hansen
Steffen Lauritzsen
Jonas Peters
Postdocs
PhD students
Rune Christiansen
Martin Jakobsen
Søren Wengel Mogensen
Lasse Petersen
Former
Frederik Riis Mikkelsen
Adam Lund