353 research outputs found
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels
of exposure to a treatment is of high practical relevance for several important
fields, such as healthcare, economics and public policy. However, existing
methods for learning to estimate counterfactual outcomes from observational
data are either focused on estimating average dose-response curves, or limited
to settings with only two treatments that do not have an associated dosage
parameter. Here, we present a novel machine-learning approach towards learning
counterfactual representations for estimating individual dose-response curves
for any number of treatments with continuous dosage parameters with neural
networks. Building on the established potential outcomes framework, we
introduce performance metrics, model selection criteria, model architectures,
and open benchmarks for estimating individual dose-response curves. Our
experiments show that the methods developed in this work set a new
state-of-the-art in estimating individual dose-response
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Counterfactual inference for continuous rather than binary treatment
variables is more common in real-world causal inference tasks. While there are
already some sample reweighting methods based on Marginal Structural Model for
eliminating the confounding bias, they generally focus on removing the
treatment's linear dependence on confounders and rely on the accuracy of the
assumed parametric models, which are usually unverifiable. In this paper, we
propose a de-confounding representation learning (DRL) framework for
counterfactual outcome estimation of continuous treatment by generating the
representations of covariates disentangled with the treatment variables. The
DRL is a non-parametric model that eliminates both linear and nonlinear
dependence between treatment and covariates. Specifically, we train the
correlations between the de-confounded representations and the treatment
variables against the correlations between the covariate representations and
the treatment variables to eliminate confounding bias. Further, a
counterfactual inference network is embedded into the framework to make the
learned representations serve both de-confounding and trusted inference.
Extensive experiments on synthetic datasets show that the DRL model performs
superiorly in learning de-confounding representations and outperforms
state-of-the-art counterfactual inference models for continuous treatment
variables. In addition, we apply the DRL model to a real-world medical dataset
MIMIC and demonstrate a detailed causal relationship between red cell width
distribution and mortality.Comment: 15 pages,4 figure
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the
current opioid epidemic in the United States. In this work, we analyze medical
and pharmaceutical claims data to draw insights on characteristics of patients
who are more prone to adverse outcomes after an initial synthetic opioid
prescription. Toward this end, we propose a generative model that allows
discovery from observational data of subgroups that demonstrate an enhanced or
diminished causal effect due to treatment. Our approach models these
sub-populations as a mixture distribution, using sparsity to enhance
interpretability, while jointly learning nonlinear predictors of the potential
outcomes to better adjust for confounding. The approach leads to
human-interpretable insights on discovered subgroups, improving the practical
utility for decision suppor
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning
with amyloidosis, followed by neuronal loss and deterioration in structure,
function, and cognition. The accumulation of amyloid-beta in the brain,
measured through 18F-florbetapir (AV45) positron emission tomography (PET)
imaging, has been widely used for early diagnosis of AD. However, the
relationship between amyloid-beta accumulation and AD pathophysiology remains
unclear, and causal inference approaches are needed to uncover how amyloid-beta
levels can impact AD development. In this paper, we propose a graph varying
coefficient neural network (GVCNet) for estimating the individual treatment
effect with continuous treatment levels using a graph convolutional neural
network. We highlight the potential of causal inference approaches, including
GVCNet, for measuring the regional causal connections between amyloid-beta
accumulation and AD pathophysiology, which may serve as a robust tool for early
diagnosis and tailored care
Counterfactual Explanations of Neural Network-Generated Response Curves
Response curves exhibit the magnitude of the response of a sensitive system
to a varying stimulus. However, response of such systems may be sensitive to
multiple stimuli (i.e., input features) that are not necessarily independent.
As a consequence, the shape of response curves generated for a selected input
feature (referred to as "active feature") might depend on the values of the
other input features (referred to as "passive features"). In this work we
consider the case of systems whose response is approximated using regression
neural networks. We propose to use counterfactual explanations (CFEs) for the
identification of the features with the highest relevance on the shape of
response curves generated by neural network black boxes. CFEs are generated by
a genetic algorithm-based approach that solves a multi-objective optimization
problem. In particular, given a response curve generated for an active feature,
a CFE finds the minimum combination of passive features that need to be
modified to alter the shape of the response curve. We tested our method on a
synthetic dataset with 1-D inputs and two crop yield prediction datasets with
2-D inputs. The relevance ranking of features and feature combinations obtained
on the synthetic dataset coincided with the analysis of the equation that was
used to generate the problem. Results obtained on the yield prediction datasets
revealed that the impact on fertilizer responsivity of passive features depends
on the terrain characteristics of each field.Comment: Accepted to appear in the International Joint Conference on Neural
Networks 202
Reliable Off-Policy Learning for Dosage Combinations
Decision-making in personalized medicine such as cancer therapy or critical
care must often make choices for dosage combinations, i.e., multiple continuous
treatments. Existing work for this task has modeled the effect of multiple
treatments independently, while estimating the joint effect has received little
attention but comes with non-trivial challenges. In this paper, we propose a
novel method for reliable off-policy learning for dosage combinations. Our
method proceeds along three steps: (1) We develop a tailored neural network
that estimates the individualized dose-response function while accounting for
the joint effect of multiple dependent dosages. (2) We estimate the generalized
propensity score using conditional normalizing flows in order to detect regions
with limited overlap in the shared covariate-treatment space. (3) We present a
gradient-based learning algorithm to find the optimal, individualized dosage
combinations. Here, we ensure reliable estimation of the policy value by
avoiding regions with limited overlap. We finally perform an extensive
evaluation of our method to show its effectiveness. To the best of our
knowledge, ours is the first work to provide a method for reliable off-policy
learning for optimal dosage combinations.Comment: Accepted at NeurIPS 202
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