Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

• URL: http://arxiv.org/abs/2105.09108v1
• Date: Wed, 19 May 2021 13:09:47 GMT
• Title: Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
• Authors: Benjie Wang, Clare Lyle, Marta Kwiatkowska
• Abstract summary: Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. We consider causal Bayesian networks and formally define the interventional robustness problem. We provide efficient algorithms for computing guaranteed upper and lower bounds on the interventional probabilities.
• Score: 20.27500901133189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical) changes in the data-generating process, whether due to natural reasons or by the action of an adversary. We consider causal Bayesian networks and formally define the interventional robustness problem, a novel model-based notion of robustness for decision functions that measures worst-case performance with respect to a set of interventions that denote changes to parameters and/or causal influences. By relying on a tractable representation of Bayesian networks as arithmetic circuits, we provide efficient algorithms for computing guaranteed upper and lower bounds on the interventional robustness probabilities. Experimental results demonstrate that the methods yield useful and interpretable bounds for a range of practical networks, paving the way towards provably causally robust decision-making systems.

Related papers

• Towards stable real-world equation discovery with assessing differentiating quality influence [52.2980614912553]
  We propose alternatives to the commonly used finite differences-based method. We evaluate these methods in terms of applicability to problems, similar to the real ones, and their ability to ensure the convergence of equation discovery algorithms.
  arXiv  Detail & Related papers  (2023-11-09T23:32:06Z)
• A Closer Look at the Intervention Procedure of Concept Bottleneck Models [18.222350428973343]
  Concept bottleneck models (CBMs) are a class of interpretable neural network models that predict the target response of a given input based on its high-level concepts. CBMs enable domain experts to intervene on the predicted concepts and rectify any mistakes at test time, so that more accurate task predictions can be made at the end. We develop various ways of selecting intervening concepts to improve the intervention effectiveness and conduct an array of in-depth analyses as to how they evolve under different circumstances.
  arXiv  Detail & Related papers  (2023-02-28T02:37:24Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects [0.6299766708197883]
  Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system. Existing methods compute the effect of recourse actions using a causal model learnt from data under the assumption of no hidden confounding and modelling assumptions such as additive noise. We propose an alternative approach for discrete random variables which relaxes these assumptions and allows for unobserved confounding and arbitrary structural equations.
  arXiv  Detail & Related papers  (2021-06-22T15:07:49Z)
• BayesIMP: Uncertainty Quantification for Causal Data Fusion [52.184885680729224]
  We study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. We introduce a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space.
  arXiv  Detail & Related papers  (2021-06-07T10:14:18Z)
• Stochastic Intervention for Causal Effect Estimation [7.015556609676951]
  We propose a new propensity score and intervention effect estimator (SIE) to estimate intervention effect. We also design a customized genetic algorithm specific to intervention effect (Ge-SIO) with the aim of providing causal evidence for decision making. Our proposed measures and algorithms can achieve a significant performance lift in comparison with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-05-27T01:12:03Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Loss Bounds for Approximate Influence-Based Abstraction [81.13024471616417]
  Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them. This paper investigates the performance of such approaches from a theoretical perspective. We show that neural networks trained with cross entropy are well suited to learn approximate influence representations.
  arXiv  Detail & Related papers  (2020-11-03T15:33:10Z)
• Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [103.14809802212535]
  We build on the generative adversarial networks (GANs) framework to address the problem of estimating the effect of continuous-valued interventions. Our model, SCIGAN, is flexible and capable of simultaneously estimating counterfactual outcomes for several different continuous interventions. To address the challenges presented by shifting to continuous interventions, we propose a novel architecture for our discriminator.
  arXiv  Detail & Related papers  (2020-02-27T18:46:21Z)

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.