15,002 research outputs found
Invariant Causal Prediction for Nonlinear Models
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)
(Peters et al., 2016) 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
modelling 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
Marginal integration for nonparametric causal inference
We consider the problem of inferring the total causal effect of a single
variable intervention on a (response) variable of interest. We propose a
certain marginal integration regression technique for a very general class of
potentially nonlinear structural equation models (SEMs) with known structure,
or at least known superset of adjustment variables: we call the procedure
S-mint regression. We easily derive that it achieves the convergence rate as
for nonparametric regression: for example, single variable intervention effects
can be estimated with convergence rate assuming smoothness with
twice differentiable functions. Our result can also be seen as a major
robustness property with respect to model misspecification which goes much
beyond the notion of double robustness. Furthermore, when the structure of the
SEM is not known, we can estimate (the equivalence class of) the directed
acyclic graph corresponding to the SEM, and then proceed by using S-mint based
on these estimates. We empirically compare the S-mint regression method with
more classical approaches and argue that the former is indeed more robust, more
reliable and substantially simpler.Comment: 40 pages, 14 figure
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from an
observational joint distribution. One can use these graphs to predict the
outcome of interventional experiments, from which data are often not available.
We show that if the observational distribution follows a structural equation
model with an additive noise structure, the directed acyclic graph becomes
identifiable from the distribution under mild conditions. This constitutes an
interesting alternative to traditional methods that assume faithfulness and
identify only the Markov equivalence class of the graph, thus leaving some
edges undirected. We provide practical algorithms for finitely many samples,
RESIT (Regression with Subsequent Independence Test) and two methods based on
an independence score. We prove that RESIT is correct in the population setting
and provide an empirical evaluation
Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to
reconstruct a latent quantity of interest. The method, referred to as
half-sibling regression, is inspired by recent work in causal inference using
additive noise models. We provide a theoretical justification and illustrate
the potential of the method in a challenging astronomy application.Comment: Extended version of a paper appearing in the Proceedings of the 32nd
International Conference on Machine Learning, Lille, France, 201
Optimal model-free prediction from multivariate time series
Forecasting a time series from multivariate predictors constitutes a
challenging problem, especially using model-free approaches. Most techniques,
such as nearest-neighbor prediction, quickly suffer from the curse of
dimensionality and overfitting for more than a few predictors which has limited
their application mostly to the univariate case. Therefore, selection
strategies are needed that harness the available information as efficiently as
possible. Since often the right combination of predictors matters, ideally all
subsets of possible predictors should be tested for their predictive power, but
the exponentially growing number of combinations makes such an approach
computationally prohibitive. Here a prediction scheme that overcomes this
strong limitation is introduced utilizing a causal pre-selection step which
drastically reduces the number of possible predictors to the most predictive
set of causal drivers making a globally optimal search scheme tractable. The
information-theoretic optimality is derived and practical selection criteria
are discussed. As demonstrated for multivariate nonlinear stochastic delay
processes, the optimal scheme can even be less computationally expensive than
commonly used sub-optimal schemes like forward selection. The method suggests a
general framework to apply the optimal model-free approach to select variables
and subsequently fit a model to further improve a prediction or learn
statistical dependencies. The performance of this framework is illustrated on a
climatological index of El Ni\~no Southern Oscillation.Comment: 14 pages, 9 figure
Optimal model-free prediction from multivariate time series
© 2015 American Physical Society.Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation
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