Max Planck Institute for Dynamics and Self-Organization -- Department for Nonlinear Dynamics and Network Dynamics Group
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Tuesday, 20.11.2012 17 c.t.

Causal Inference on Time Series using Structural Equation Models

by Jonas Peters
from Seminar for Statistics, ETH Zurich, Switzerland

Contact person: Rainer Engelken


Ludwig Prandtl lecture hall


Causal inference uses observations to infer the causal structure of the data generating system. We study a class of structural equation models that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional methods like Granger causality exploit the variance of residuals. There are two main contributions: (1) Theoretical: By restricting the model class (e.g. to additive noise) we can provide a more general identifiability result than existing ones. This result incorporates lagged and instantaneous effects that can be nonlinear and do not need to be faithful, and non-instantaneous feedback between the time series. (2) Practical: If there are no feedback loops between time series, we propose an algorithm based on non-linear independence tests of time series. When the data are causally insufficient, or the data generating process does not satisfy the model assumptions, this algorithm may still give partial results, but mostly avoids incorrect answers.

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