2,506 research outputs found
Adversarial De-confounding in Individualised Treatment Effects Estimation
Observational studies have recently received significant attention from the
machine learning community due to the increasingly available non-experimental
observational data and the limitations of the experimental studies, such as
considerable cost, impracticality, small and less representative sample sizes,
etc. In observational studies, de-confounding is a fundamental problem of
individualised treatment effects (ITE) estimation. This paper proposes
disentangled representations with adversarial training to selectively balance
the confounders in the binary treatment setting for the ITE estimation. The
adversarial training of treatment policy selectively encourages
treatment-agnostic balanced representations for the confounders and helps to
estimate the ITE in the observational studies via counterfactual inference.
Empirical results on synthetic and real-world datasets, with varying degrees of
confounding, prove that our proposed approach improves the state-of-the-art
methods in achieving lower error in the ITE estimation.Comment: accepted to AISTATS 202
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting
modality for COVID-19 patients who are refractory to conventional therapies.
However, the proper treatment decision has been the subject of significant
debate and it remains controversial about who benefits from this scarcely
available and technically complex treatment option. To support clinical
decisions, it is a critical need to predict the treatment need and the
potential treatment and no-treatment responses. Targeting this clinical
challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel
approach for individualized treatment analysis. TVAE is specifically designed
to address the modeling challenges like ECMO with strong treatment selection
bias and scarce treatment cases. TVAE conceptualizes the treatment decision as
a multi-scale problem. We model a patient's potential treatment assignment and
the factual and counterfactual outcomes as part of their intrinsic
characteristics that can be represented by a deep latent variable model. The
factual and counterfactual prediction errors are alleviated via a
reconstruction regularization scheme together with semi-supervision, and the
selection bias and the scarcity of treatment cases are mitigated by the
disentangled and distribution-matched latent space and the label-balancing
generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an
international dataset collected from 1651 hospitals across 63 countries, and a
institutional dataset collected from 15 hospitals. The results show that TVAE
outperforms state-of-the-art treatment effect models in predicting both the
propensity scores and factual outcomes on heterogeneous COVID-19 datasets.
Additional experiments also show TVAE outperforms the best existing models in
individual treatment effect estimation on the synthesized IHDP benchmark
dataset
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work
Handling non-ignorable dropouts in longitudinal data: A conditional model based on a latent Markov heterogeneity structure
We illustrate a class of conditional models for the analysis of longitudinal
data suffering attrition in random effects models framework, where the
subject-specific random effects are assumed to be discrete and to follow a
time-dependent latent process. The latent process accounts for unobserved
heterogeneity and correlation between individuals in a dynamic fashion, and for
dependence between the observed process and the missing data mechanism. Of
particular interest is the case where the missing mechanism is non-ignorable.
To deal with the topic we introduce a conditional to dropout model. A shape
change in the random effects distribution is considered by directly modeling
the effect of the missing data process on the evolution of the latent
structure. To estimate the resulting model, we rely on the conditional maximum
likelihood approach and for this aim we outline an EM algorithm. The proposal
is illustrated via simulations and then applied on a dataset concerning skin
cancers. Comparisons with other well-established methods are provided as well
NAISR: A 3D Neural Additive Model for Interpretable Shape Representation
Deep implicit functions (DIFs) have emerged as a powerful paradigm for many
computer vision tasks such as 3D shape reconstruction, generation,
registration, completion, editing, and understanding. However, given a set of
3D shapes with associated covariates there is at present no shape
representation method which allows to precisely represent the shapes while
capturing the individual dependencies on each covariate. Such a method would be
of high utility to researchers to discover knowledge hidden in a population of
shapes. We propose a 3D Neural Additive Model for Interpretable Shape
Representation (NAISR) which describes individual shapes by deforming a shape
atlas in accordance to the effect of disentangled covariates. Our approach
captures shape population trends and allows for patient-specific predictions
through shape transfer. NAISR is the first approach to combine the benefits of
deep implicit shape representations with an atlas deforming according to
specified covariates. Although our driving problem is the construction of an
airway atlas, NAISR is a general approach for modeling, representing, and
investigating shape populations. We evaluate NAISR with respect to shape
reconstruction, shape disentanglement, shape evolution, and shape transfer for
the pediatric upper airway. Our experiments demonstrate that NAISR achieves
competitive shape reconstruction performance while retaining interpretability.Comment: 20 page
Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data
Estimating treatment effects, especially individualized treatment effects
(ITE), using observational data is challenging due to the complex situations of
confounding bias. Existing approaches for estimating treatment effects from
longitudinal observational data are usually built upon a strong assumption of
"unconfoundedness", which is hard to fulfill in real-world practice. In this
paper, we propose the Variational Temporal Deconfounder (VTD), an approach that
leverages deep variational embeddings in the longitudinal setting using proxies
(i.e., surrogate variables that serve for unobservable variables).
Specifically, VTD leverages observed proxies to learn a hidden embedding that
reflects the true hidden confounders in the observational data. As such, our
VTD method does not rely on the "unconfoundedness" assumption. We test our VTD
method on both synthetic and real-world clinical data, and the results show
that our approach is effective when hidden confounding is the leading bias
compared to other existing models
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
The problem of fair classification can be mollified if we develop a method to
remove the embedded sensitive information from the classification features.
This line of separating the sensitive information is developed through the
causal inference, and the causal inference enables the counterfactual
generations to contrast the what-if case of the opposite sensitive attribute.
Along with this separation with the causality, a frequent assumption in the
deep latent causal model defines a single latent variable to absorb the entire
exogenous uncertainty of the causal graph. However, we claim that such
structure cannot distinguish the 1) information caused by the intervention
(i.e., sensitive variable) and 2) information correlated with the intervention
from the data. Therefore, this paper proposes Disentangled Causal Effect
Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling
the exogenous uncertainty into two latent variables: either 1) independent to
interventions or 2) correlated to interventions without causality.
Particularly, our disentangling approach preserves the latent variable
correlated to interventions in generating counterfactual examples. We show that
our method estimates the total effect and the counterfactual effect without a
complete causal graph. By adding a fairness regularization, DCEVAE generates a
counterfactual fair dataset while losing less original information. Also,
DCEVAE generates natural counterfactual images by only flipping sensitive
information. Additionally, we theoretically show the differences in the
covariance structures of DCEVAE and prior works from the perspective of the
latent disentanglement
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