10,727 research outputs found
Diffusion Causal Models for Counterfactual Estimation
We consider the task of counterfactual estimation from observational imaging
data given a known causal structure. In particular, quantifying the causal
effect of interventions for high-dimensional data with neural networks remains
an open challenge. Herein we propose Diff-SCM, a deep structural causal model
that builds on recent advances of generative energy-based models. In our
setting, inference is performed by iteratively sampling gradients of the
marginal and conditional distributions entailed by the causal model.
Counterfactual estimation is achieved by firstly inferring latent variables
with deterministic forward diffusion, then intervening on a reverse diffusion
process using the gradients of an anti-causal predictor w.r.t the input.
Furthermore, we propose a metric for evaluating the generated counterfactuals.
We find that Diff-SCM produces more realistic and minimal counterfactuals than
baselines on MNIST data and can also be applied to ImageNet data. Code is
available https://github.com/vios-s/Diff-SCM.Comment: Accepted at CLeaR (Causal Learning and Reasoning) 202
Detecting and quantifying causal associations in large nonlinear time series datasets
Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields
Causal Effect Inference with Deep Latent-Variable Models
Learning individual-level causal effects from observational data, such as
inferring the most effective medication for a specific patient, is a problem of
growing importance for policy makers. The most important aspect of inferring
causal effects from observational data is the handling of confounders, factors
that affect both an intervention and its outcome. A carefully designed
observational study attempts to measure all important confounders. However,
even if one does not have direct access to all confounders, there may exist
noisy and uncertain measurement of proxies for confounders. We build on recent
advances in latent variable modeling to simultaneously estimate the unknown
latent space summarizing the confounders and the causal effect. Our method is
based on Variational Autoencoders (VAE) which follow the causal structure of
inference with proxies. We show our method is significantly more robust than
existing methods, and matches the state-of-the-art on previous benchmarks
focused on individual treatment effects.Comment: Published as a conference paper at NIPS 201
Causal Confusion in Imitation Learning
Behavioral cloning reduces policy learning to supervised learning by training
a discriminative model to predict expert actions given observations. Such
discriminative models are non-causal: the training procedure is unaware of the
causal structure of the interaction between the expert and the environment. We
point out that ignoring causality is particularly damaging because of the
distributional shift in imitation learning. In particular, it leads to a
counter-intuitive "causal misidentification" phenomenon: access to more
information can yield worse performance. We investigate how this problem
arises, and propose a solution to combat it through targeted
interventions---either environment interaction or expert queries---to determine
the correct causal model. We show that causal misidentification occurs in
several benchmark control domains as well as realistic driving settings, and
validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice
Telling Cause from Effect using MDL-based Local and Global Regression
We consider the fundamental problem of inferring the causal direction between
two univariate numeric random variables and from observational data.
The two-variable case is especially difficult to solve since it is not possible
to use standard conditional independence tests between the variables.
To tackle this problem, we follow an information theoretic approach based on
Kolmogorov complexity and use the Minimum Description Length (MDL) principle to
provide a practical solution. In particular, we propose a compression scheme to
encode local and global functional relations using MDL-based regression. We
infer causes in case it is shorter to describe as a function of
than the inverse direction. In addition, we introduce Slope, an efficient
linear-time algorithm that through thorough empirical evaluation on both
synthetic and real world data we show outperforms the state of the art by a
wide margin.Comment: 10 pages, To appear in ICDM1
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