The Representation of Causality for Auditing

Project Details

Description

This project will combine logic and probability theory to develop new

methods and decision aids for causal reasoning. We will use a new

axiomatization of the classical idea of a probability tree, which goes well

beyond the current paradigm of Bayes nets in allowing modular

representation of probabilistic information. Financial statement auditing

will be used as a test bed for the methods, which will ultimately be

applicable to many domains where expert systems are used.

The goal of an audit is the expression of an opinion on whether a set of

financial statements presents a business's financial position and

operations fairly in all material respects, in accordance with generally

accepted accounting principles. In reaching this opinion, the auditor may

need to consider a variety of subsidiary questions: whether items such as

the value of inventory are materially correct; whether internal control is

operating effectively; whether the business can continue as a going

concern, etc. The evidence bearing on these questions evidence is usually

persuasive but not conclusive, and its assessment must be based on a causal

model of the business, its environment, and the audit.

Prior research has focused on representing and aggregating uncertainty

within a model. Although this research has met its own goals reasonably

well, the model itself is almost always either overly simplified or else

very specialized, and hence the research has not produced decision aids

with the flexibility to serve auditors going into new engagements. Such

aids would have to help auditors build causal models, a task they find

challenging.

Constructing adequate models means refining models. We always begin an

audit with a simplified model, but the audit constantly raises new issues

that must be incorporated into this model. The modularity allowed by our

approach permits this constant refinement. We will develop an inference

engine that permits such refinement, together with detailed casual models

for an illustrative group of audit problems and prototype decision aids

that may provide a test-bed for future empirical research on audit judgment.

Probabilistic models currently available to auditors are neither simple

enough to be used effectively nor complex enough to fit practical auditing

realities. This project will overcome these limitations by developing a

clear, rigorous and usable concept of refinement. When new evidence

reveals that factors previously deemed unimportant are, in fact, relevant,

they can be taken into account by refining the existing model, rather than

replacing it. This ability to reason simultaneously and consistently with

causal analyses at different levels of detail will allow the auditor to

avoid models that are hopelessly naive or impossible complex

StatusFinished
Effective start/end date7/1/9912/31/02

Funding

  • National Science Foundation: $353,114.00

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