18,227 research outputs found
Mutual Information and Minimum Mean-square Error in Gaussian Channels
This paper deals with arbitrarily distributed finite-power input signals
observed through an additive Gaussian noise channel. It shows a new formula
that connects the input-output mutual information and the minimum mean-square
error (MMSE) achievable by optimal estimation of the input given the output.
That is, the derivative of the mutual information (nats) with respect to the
signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input
statistics. This relationship holds for both scalar and vector signals, as well
as for discrete-time and continuous-time noncausal MMSE estimation. This
fundamental information-theoretic result has an unexpected consequence in
continuous-time nonlinear estimation: For any input signal with finite power,
the causal filtering MMSE achieved at SNR is equal to the average value of the
noncausal smoothing MMSE achieved with a channel whose signal-to-noise ratio is
chosen uniformly distributed between 0 and SNR
Distinguishing cause from effect using observational data: methods and benchmarks
The discovery of causal relationships from purely observational data is a
fundamental problem in science. The most elementary form of such a causal
discovery problem is to decide whether X causes Y or, alternatively, Y causes
X, given joint observations of two variables X, Y. An example is to decide
whether altitude causes temperature, or vice versa, given only joint
measurements of both variables. Even under the simplifying assumptions of no
confounding, no feedback loops, and no selection bias, such bivariate causal
discovery problems are challenging. Nevertheless, several approaches for
addressing those problems have been proposed in recent years. We review two
families of such methods: Additive Noise Methods (ANM) and Information
Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs
that consists of data for 100 different cause-effect pairs selected from 37
datasets from various domains (e.g., meteorology, biology, medicine,
engineering, economy, etc.) and motivate our decisions regarding the "ground
truth" causal directions of all pairs. We evaluate the performance of several
bivariate causal discovery methods on these real-world benchmark data and in
addition on artificially simulated data. Our empirical results on real-world
data indicate that certain methods are indeed able to distinguish cause from
effect using only purely observational data, although more benchmark data would
be needed to obtain statistically significant conclusions. One of the best
performing methods overall is the additive-noise method originally proposed by
Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of
0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of
this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning
Researc
Causal Inference on Discrete Data using Additive Noise Models
Inferring the causal structure of a set of random variables from a finite
sample of the joint distribution is an important problem in science. Recently,
methods using additive noise models have been suggested to approach the case of
continuous variables. In many situations, however, the variables of interest
are discrete or even have only finitely many states. In this work we extend the
notion of additive noise models to these cases. We prove that whenever the
joint distribution \prob^{(X,Y)} admits such a model in one direction, e.g.
Y=f(X)+N, N \independent X, it does not admit the reversed model
X=g(Y)+\tilde N, \tilde N \independent Y as long as the model is chosen in a
generic way. Based on these deliberations we propose an efficient new algorithm
that is able to distinguish between cause and effect for a finite sample of
discrete variables. In an extensive experimental study we show that this
algorithm works both on synthetic and real data sets
Causal Inference by Stochastic Complexity
The algorithmic Markov condition states that the most likely causal direction
between two random variables X and Y can be identified as that direction with
the lowest Kolmogorov complexity. Due to the halting problem, however, this
notion is not computable.
We hence propose to do causal inference by stochastic complexity. That is, we
propose to approximate Kolmogorov complexity via the Minimum Description Length
(MDL) principle, using a score that is mini-max optimal with regard to the
model class under consideration. This means that even in an adversarial
setting, such as when the true distribution is not in this class, we still
obtain the optimal encoding for the data relative to the class.
We instantiate this framework, which we call CISC, for pairs of univariate
discrete variables, using the class of multinomial distributions. Experiments
show that CISC is highly accurate on synthetic, benchmark, as well as
real-world data, outperforming the state of the art by a margin, and scales
extremely well with regard to sample and domain sizes
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probability
distributions. In particular, we assume access to a collection
, where each is a sample drawn from the
probability distribution of , and is a binary label
indicating whether "" or "". Given these data,
we build a causal inference rule in two steps. First, we featurize each
using the kernel mean embedding associated with some characteristic kernel.
Second, we train a binary classifier on such embeddings to distinguish between
causal directions. We present generalization bounds showing the statistical
consistency and learning rates of the proposed approach, and provide a simple
implementation that achieves state-of-the-art cause-effect inference.
Furthermore, we extend our ideas to infer causal relationships between more
than two variables
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