Adversarial Distribution Balancing for Counterfactual Reasoning
- URL: http://arxiv.org/abs/2311.16616v1
- Date: Tue, 28 Nov 2023 09:12:37 GMT
- Title: Adversarial Distribution Balancing for Counterfactual Reasoning
- Authors: Stefan Schrod, Fabian Sinz, Michael Altenbuchinger
- Abstract summary: Machine learning approaches for counterfactual reasoning have to deal with both unobserved outcomes and distributional differences due to non-random treatment administration.
We propose Adversarial Distribution Balancing for Counterfactual Reasoning (ADBCR) to remove spurious causal relations.
We show that ADBCR outcompetes state-of-the-art methods on three benchmark datasets.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of causal prediction models is challenged by the fact that
the outcome is only observable for the applied (factual) intervention and not
for its alternatives (the so-called counterfactuals); in medicine we only know
patients' survival for the administered drug and not for other therapeutic
options. Machine learning approaches for counterfactual reasoning have to deal
with both unobserved outcomes and distributional differences due to non-random
treatment administration. Unsupervised domain adaptation (UDA) addresses
similar issues; one has to deal with unobserved outcomes -- the labels of the
target domain -- and distributional differences between source and target
domain. We propose Adversarial Distribution Balancing for Counterfactual
Reasoning (ADBCR), which directly uses potential outcome estimates of the
counterfactuals to remove spurious causal relations. We show that ADBCR
outcompetes state-of-the-art methods on three benchmark datasets, and
demonstrate that ADBCR's performance can be further improved if unlabeled
validation data are included in the training procedure to better adapt the
model to the validation domain.
Related papers
- Multivariate and Online Transfer Learning with Uncertainty Quantification [1.1588856557881027]
Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can lead to tooth loss.
We propose an extension to RECaST Bayesian transfer learning framework.
Negative transfer is mitigated to ensure that the information shared from the other demographic groups does not negatively impact the modeling of the underrepresented participants.
arXiv Detail & Related papers (2024-11-19T15:14:13Z) - Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments [0.0]
This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments.
The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables.
arXiv Detail & Related papers (2024-09-10T15:34:48Z) - Unsupervised Domain Adaptation for Brain Vessel Segmentation through
Transwarp Contrastive Learning [46.248404274124546]
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
This paper proposes a simple yet potent contrastive learning framework for UDA to narrow the inter-domain gap between labelled source and unlabelled target distribution.
arXiv Detail & Related papers (2024-02-23T10:01:22Z) - Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data [62.56890808004615]
We develop an interpretable method for distributional data analysis that ensures trustworthy and robust decision-making.
We demonstrate ADD MALTS' utility by studying the effectiveness of continuous glucose monitors in mitigating diabetes risks.
arXiv Detail & Related papers (2023-12-17T00:42:42Z) - Nonparametric Identifiability of Causal Representations from Unknown
Interventions [63.1354734978244]
We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables.
Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
arXiv Detail & Related papers (2023-06-01T10:51:58Z) - Learning Unbiased Transferability for Domain Adaptation by Uncertainty
Modeling [107.24387363079629]
Domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled or a less labeled but related target domain.
Due to the imbalance between the amount of annotated data in the source and target domains, only the target distribution is aligned to the source domain.
We propose a non-intrusive Unbiased Transferability Estimation Plug-in (UTEP) by modeling the uncertainty of a discriminator in adversarial-based DA methods to optimize unbiased transfer.
arXiv Detail & Related papers (2022-06-02T21:58:54Z) - Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach [0.0]
Dynamic Treatment Regimes (DTRs) are widely studied to formalize this process.
We develop Reinforcement Learning methods to efficiently learn optimal treatment regimes.
arXiv Detail & Related papers (2021-12-08T20:22:04Z) - Efficient Causal Inference from Combined Observational and
Interventional Data through Causal Reductions [68.6505592770171]
Unobserved confounding is one of the main challenges when estimating causal effects.
We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders.
We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
arXiv Detail & Related papers (2021-03-08T14:29:07Z) - Selecting Treatment Effects Models for Domain Adaptation Using Causal
Knowledge [82.5462771088607]
We propose a novel model selection metric specifically designed for ITE methods under the unsupervised domain adaptation setting.
In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain.
arXiv Detail & Related papers (2021-02-11T21:03:14Z) - Split-Treatment Analysis to Rank Heterogeneous Causal Effects for
Prospective Interventions [15.443178111068418]
We propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention.
We show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect.
arXiv Detail & Related papers (2020-11-11T16:17:29Z) - Learning Overlapping Representations for the Estimation of
Individualized Treatment Effects [97.42686600929211]
Estimating the likely outcome of alternatives from observational data is a challenging problem.
We show that algorithms that learn domain-invariant representations of inputs are often inappropriate.
We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
arXiv Detail & Related papers (2020-01-14T12:56:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.