3,804 research outputs found
Learning how to act: making good decisions with machine learning
This thesis is about machine learning and statistical approaches
to decision making. How can we learn from data to anticipate the
consequence of, and optimally select, interventions or actions?
Problems such as deciding which medication to prescribe to
patients, who should be released on bail, and how much to charge
for insurance are ubiquitous, and have far reaching impacts on
our lives. There are two fundamental approaches to learning how
to act: reinforcement learning, in which an agent directly
intervenes in a system and learns from the outcome, and
observational causal inference, whereby we seek to infer the
outcome of an intervention from observing the system.
The goal of this thesis to connect and unify these key
approaches. I introduce causal bandit problems: a synthesis that
combines causal graphical models, which were developed for
observational causal inference, with multi-armed bandit problems,
which are a subset of reinforcement learning problems that are
simple enough to admit formal analysis. I show that knowledge of
the causal structure allows us to transfer information learned
about the outcome of one action to predict the outcome of an
alternate action, yielding a novel form of structure between
bandit arms that cannot be exploited by existing algorithms. I
propose an algorithm for causal bandit problems and prove bounds
on the simple regret demonstrating it is close to mini-max
optimal and better than algorithms that do not use the additional
causal information
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Bayesian Structural Causal Inference with Probabilistic Programming
Reasoning about causal relationships is central to the human experience. This evokes a natural question in our pursuit of human-like artificial intelligence: how might we imbue intelligent systems with similar causal reasoning capabilities? Better yet, how might we imbue intelligent systems with the ability to learn cause and effect relationships from observation and experimentation? Unfortunately, reasoning about cause and effect requires more than just data: it also requires partial knowledge about data generating mechanisms. Given this need, our task then as computational scientists is to design data structures for representing partial causal knowledge, and algorithms for updating that knowledge in light of observations and experiments. In this dissertation, I explore the Bayesian structural approach to causal inference in which probability distributions over structural causal models are one such data structure, and probabilistic inference in multi-world transformations of those models as the corresponding algorithmic task. Specifically, I demonstrate that this approach has two distinct advantages over the dominant computational paradigm of causal graphical models: (i) it expands the breadth of compatible assumptions; and (ii) it seamlessly integrates with modern Bayesian modeling and inference technologies to facilitate quantification of uncertainty about causal structure and the effects of interventions.
Specifically, doing so allows the emerging and powerful technology of probabilistic programming to be brought to bear on a large and diverse set of causal inference problems. In Chapter 3, I present an example-driven pedagogical introduction to the Bayesian structural approach to causal inference, demonstrating how priors over structural causal models induce joint distributions over observed and latent counterfactual random variables, and how the resulting posterior distributions capture common motifs in causal inference. In particular, I show how various assumptions about latent confounding influence our ability to estimate causal effects from data and I provide examples of common observational and quasi-experimental designs expressed as probabilistic programs. In Chapter 4, I present an advanced application of the Bayesian structural approach for modeling hierarchical relational dependencies with latent confounders, and how to combine such assumptions with flexible Gaussian process models. In Chapter 5, I present a prototype software implementation for causal inference using probabilistic programming, accommodating a broad class of multi-source observational and experimental data. Finally, in Chapter 6, I present Simulation-Based Identifiability, a gradient-based optimization method for determining if any differentiable and bounded prior over structural causal models converges to a unique causal conclusion asymptotically
Enterprise search and discovery capability: the factors and generative mechanisms for user satisfaction.
Many organizations are re-creating the 'Google-like' experience behind their firewall to exploit their information. However, surveys show dissatisfaction with enterprise search is commonplace. No prior study has investigated unsolicited user feedback from an enterprise search user interface to understand the underlying reasons for dissatisfaction. A mixed methods longitudinal study was undertaken analysing feedback from over 1,000 users and interviewing search service staff in a multinational corporation. Results show that 62% of dissatisfaction events were due to human (information & search literacy) rather than technology factors. Cognitive biases and the 'Google Habitus' influence expectations and information behaviour, and are postulated as deep underlying generative mechanisms. The current literature focuses on 'structure' (technology and information quality) as the reason for enterprise search satisfaction, agency (search literacy) appears downplayed. Organizations which emphasise 'systems thinking' and bimodal approaches towards search strategy and information behaviour may improve capabilities
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Probabilistic (Bayesian) modeling has experienced a surge of applications in
almost all quantitative sciences and industrial areas. This development is
driven by a combination of several factors, including better probabilistic
estimation algorithms, flexible software, increased computing power, and a
growing awareness of the benefits of probabilistic learning. However, a
principled Bayesian model building workflow is far from complete and many
challenges remain. To aid future research and applications of a principled
Bayesian workflow, we ask and provide answers for what we perceive as two
fundamental questions of Bayesian modeling, namely (a) "What actually is a
Bayesian model?" and (b) "What makes a good Bayesian model?". As an answer to
the first question, we propose the PAD model taxonomy that defines four basic
kinds of Bayesian models, each representing some combination of the assumed
joint distribution of all (known or unknown) variables (P), a posterior
approximator (A), and training data (D). As an answer to the second question,
we propose ten utility dimensions according to which we can evaluate Bayesian
models holistically, namely, (1) causal consistency, (2) parameter
recoverability, (3) predictive performance, (4) fairness, (5) structural
faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9)
estimation speed, and (10) robustness. Further, we propose two example utility
decision trees that describe hierarchies and trade-offs between utilities
depending on the inferential goals that drive model building and testing
Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.
Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance
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Use of External Representations in Reasoning about Causality
This research investigated if diagrams aid in deductive reasoning with formal causal models. Four studies were conducted exploring participants' ability to discover causal paths, identify causes and effects, and create alternative explanations for variable relationships. In Study 1, abstract variables of the causal model were compared to contextually grounded variables and causal models presented as text or diagrams were compared. Participants given abstract diagrams did better in most tasks than participants in the other conditions, who all did similarly. Studies 2 and 3 compared causal models expressed in text to diagrammed causal models, and compared models using arrows to models using words when connecting variables. Participants who had arrowheads replaced with words made more errors than participants in other diagram conditions. Diagrammed causal models led to better performance than did other conditions, and there was no difference between different text models. Studies 4 and 5 tested the hypothesis that predictive reasoning (from cause to effect) is easier than diagnostic reasoning (from effect to cause). The two studies did not find any such effec
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