464 research outputs found
Optimal Restricted Estimation for More Efficient Longitudinal Causal Inference
Efficient semiparametric estimation of longitudinal causal effects is often analytically or computationally intractable. We propose a novel restricted estimation approach for increasing efficiency, which can be used with other techniques, is straightforward to implement, and requires no additional modeling assumptions
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions between
protected sub-populations; individual fairness requires treating similar
individuals alike. These two objectives, however, are incompatible when a
scoring model is calibrated through discontinuous probability functions, where
individuals can be randomly assigned an outcome determined by a fixed
probability. This procedure may provide two similar individuals from the same
protected group with classification odds that are disparately different -- a
clear violation of individual fairness. Assigning unique odds to each protected
sub-population may also prevent members of one sub-population from ever
receiving equal chances of a positive outcome to another, which we argue is
another type of unfairness called individual odds. We reconcile all this by
constructing continuous probability functions between group thresholds that are
constrained by their Lipschitz constant. Our solution preserves the model's
predictive power, individual fairness and robustness while ensuring group
fairness.Comment: 23 pages, 5 figures, 2 table
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a
powerful tool to make artificial intelligence systems explainable.
Conceptually, given an individual classified as y -- the factual -- we seek
actions such that their prediction becomes the desired class y' -- the
counterfactual. This process offers algorithmic recourse that is (1) easy to
customise and interpret, and (2) directly aligned with the goals of each
individual. However, the properties of a "good" counterfactual are still
largely debated; it remains an open challenge to effectively locate a
counterfactual along with its corresponding recourse. Some strategies use
gradient-driven methods, but these offer no guarantees on the feasibility of
the recourse and are open to adversarial attacks on carefully created
manifolds. This can lead to unfairness and lack of robustness. Other methods
are data-driven, which mostly addresses the feasibility problem at the expense
of privacy, security and secrecy as they require access to the entire training
data set. Here, we introduce LocalFACE, a model-agnostic technique that
composes feasible and actionable counterfactual explanations using
locally-acquired information at each step of the algorithmic recourse. Our
explainer preserves the privacy of users by only leveraging data that it
specifically requires to construct actionable algorithmic recourse, and
protects the model by offering transparency solely in the regions deemed
necessary for the intervention.Comment: 7 pages, 5 figures, 3 appendix page
Surrogate Markers for Time-Varying Treatments and Outcomes
BACKGROUND: A surrogate marker is a variable commonly used in clinical trials to guide treatment decisions when the outcome of ultimate interest is not available. A good surrogate marker is one where the treatment effect on the surrogate is a strong predictor of the effect of treatment on the outcome. We review the situation when there is one treatment delivered at baseline, one surrogate measured at one later time point, and one ultimate outcome of interest and discuss new issues arising when variables are time-varying.
METHODS: Most of the literature on surrogate markers has only considered simple settings with one treatment, one surrogate, and one outcome of interest at a fixed time point. However, more complicated time-varying settings are common in practice. In this article, we describe the unique challenges in two settings, time-varying treatments and time-varying surrogates, while relating the ideas back to the causal-effects and causal-association paradigms.
CONCLUSION: In addition to discussing and extending popular notions of surrogacy to time-varying settings, we give examples illustrating that one can be misled by not taking into account time-varying information about the surrogate or treatment. We hope this article has provided some motivation for future work on estimation and inference in such settings
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