Calculating the Likelihood Ratio for Multiple Pieces of Evidence

TL;DR

This paper discusses how to compute the likelihood ratio for multiple dependent pieces of forensic evidence using causal Bayesian networks, enabling automated derivation of LR in complex cases.

Contribution

It introduces a method to calculate likelihood ratios for multiple dependent evidence pieces by modeling them as a causal Bayesian network.

Findings

Likelihood ratios can be derived automatically using Bayesian network software.

The method handles multiple dependent evidence components.

It improves the interpretability of forensic evidence analysis.

Abstract

When presenting forensic evidence, such as a DNA match, experts often use the Likelihood ratio (LR) to explain the impact of evidence . The LR measures the probative value of the evidence with respect to a single hypothesis such as 'DNA comes from the suspect', and is defined as the probability of the evidence if the hypothesis is true divided by the probability of the evidence if the hypothesis is false. The LR is a valid measure of probative value because, by Bayes Theorem, the higher the LR is, the more our belief in the probability the hypothesis is true increases after observing the evidence. The LR is popular because it measures the probative value of evidence without having to make any explicit assumptions about the prior probability of the hypothesis. However, whereas the LR can in principle be easily calculated for a distinct single piece of evidence that relates directly to a specific hypothesis, in most realistic situations 'the evidence' is made up of multiple dependent components that impact multiple different hypotheses. In such situations the LR cannot be calculated . However, once the multiple pieces of evidence and hypotheses are modelled as a causal Bayesian network (BN), any relevant LR can be automatically derived using any BN software application.