The causal testing framework

Scientific models possess several properties that make them notoriously difficult to test, including a complex input space, long execution times, and non-determinism, rendering existing testing techniques impractical. In fields such as epidemiology, where researchers seek answers to challenging causal questions, a statistical methodology known as Causal Inference (CI) (Hernán & Robins, 2020; Pearl, 2009) has addressed similar problems, enabling the inference of causal conclusions from noisy, biased, and sparse observational data instead of costly randomised trials. CI works by using domain knowledge to identify and mitigate for biases in the data, enabling them to answer causal questions that concern the effect of changing some feature on the observed outcome. The Causal Testing Framework (CTF) is a software testing framework that uses CI techniques to establish causal effects between software variables from pre-existing runtime data rather than having to collect bespoke, highly curated datasets especially for testing

Experimental dipole moments for nonisolatable acetic acid structures in a nonpolar medium. A combined spectroscopic, dielectric, and DFT study for self-association in solution

10.1021/jp800609wJournal of Physical Chemistry B112206448-6459JPCB

The combination of deconvolution and density functional theory for the mid-infrared vibrational spectra of stable and unstable rhodium carbonyl clusters

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