38,333 research outputs found
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology
Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70
(1983) 41--55] as central tools to help assess the causal effects of
interventions. Since their introduction more than two decades ago, they have
found wide application in a variety of areas, including medical research,
economics, epidemiology and education, especially in those situations where
randomized experiments are either difficult to perform, or raise ethical
questions, or would require extensive delays before answers could be obtained.
In the past few years, the number of published applications using propensity
score methods to evaluate medical and epidemiological interventions has
increased dramatically. Nevertheless, thus far, we believe that there have been
few applications of propensity score methods to evaluate marketing
interventions (e.g., advertising, promotions), where the tradition is to use
generally inappropriate techniques, which focus on the prediction of an outcome
from background characteristics and an indicator for the intervention using
statistical tools such as least-squares regression, data mining, and so on.
With these techniques, an estimated parameter in the model is used to estimate
some global ``causal'' effect. This practice can generate grossly incorrect
answers that can be self-perpetuating: polishing the Ferraris rather than the
Jeeps ``causes'' them to continue to win more races than the Jeeps
visiting the high-prescribing doctors rather than the
low-prescribing doctors ``causes'' them to continue to write more
prescriptions. This presentation will take ``causality'' seriously, not just as
a casual concept implying some predictive association in a data set, and will
illustrate why propensity score methods are generally superior in practice to
the standard predictive approaches for estimating causal effects.Comment: Published at http://dx.doi.org/10.1214/088342306000000259 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Artificial Intelligence and Patient-Centered Decision-Making
Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient
Predictive User Modeling with Actionable Attributes
Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches
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