IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v6y2018i2p35n2.html
   My bibliography  Save this article

Invariant Causal Prediction for Nonlinear Models

Author

Listed:
  • Heinze-Deml Christina

    (Seminar für Statistik, ETH Zurich, Zurich, Switzerland)

  • Peters Jonas

    (University of Copenhagen, Department of Mathematics, Copenhagen, Denmark)

  • Meinshausen Nicolai

    (Seminar für Statistik, ETH Zurich, Zurich, Switzerland)

Abstract

An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure. To this end, Invariant Causal Prediction (ICP) [1] has been proposed which learns a causal model exploiting the invariance of causal relations using data from different environments. When considering linear models, the implementation of ICP is relatively straightforward. However, the nonlinear case is more challenging due to the difficulty of performing nonparametric tests for conditional independence.In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables. We find that an approach which first fits a nonlinear model with data pooled over all environments and then tests for differences between the residual distributions across environments is quite robust across a large variety of simulation settings. We call this procedure “invariant residual distribution test”. In general, we observe that the performance of all approaches is critically dependent on the true (unknown) causal structure and it becomes challenging to achieve high power if the parental set includes more than two variables.As a real-world example, we consider fertility rate modeling which is central to world population projections. We explore predicting the effect of hypothetical interventions using the accepted models from nonlinear ICP. The results reaffirm the previously observed central causal role of child mortality rates.

Suggested Citation

  • Heinze-Deml Christina & Peters Jonas & Meinshausen Nicolai, 2018. "Invariant Causal Prediction for Nonlinear Models," Journal of Causal Inference, De Gruyter, vol. 6(2), pages 1-35, September.
  • Handle: RePEc:bpj:causin:v:6:y:2018:i:2:p:35:n:2
    DOI: 10.1515/jci-2017-0016
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2017-0016
    Download Restriction: no

    File URL: https://libkey.io/10.1515/jci-2017-0016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:causin:v:6:y:2018:i:2:p:35:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.