Causal discovery to understand hot corrosion

Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or codependencies between these factors. The causal discovery algorithm fast causal inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified salt deposition flux as the most influential corrosion variable for this limited data set. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus, FCI generated causal links aligned with literature from a randomised corrosion data set, while also identifying the presence of two different degradation modes in operation

Pragmatic Causal Inference

Data-driven causal inference from real-world multivariate systems can be biased for a number of reasons. These include unmeasured confounding, systematic censoring of observations, data dependence induced by a network of unit interactions, and misspecification of parametric models. This dissertation proposes statistical methods spanning three major steps of the causal inference workflow -- discovery of a suitable causal model, which in our case, can be visualized via one of several classes of causal graphical models, identification of target causal parameters as functions of the observed data distribution, and estimation of these parameters from finite samples. The overarching goal of these methods is to augment the data scientist's toolkit to tackle the aforementioned challenges in real-world systems in theoretically sound yet practical ways. We provide a continuous optimization procedure for causal discovery in the presence of latent confounders, and a computationally efficient discrete search procedure for discovery and downstream estimation of causal effects in causal graphs encoding interactions between units in a network. For identification, we provide an algorithm that generalizes the state-of-the-art for recovery of target parameters in missing not at random distributions that can be represented graphically via directed acyclic graphs. Finally for estimation, we provide results on the tangent space of causal graphical models with latent variables which may be used to improve the efficiency of semiparametric estimators for any target parameter of interest. We also provide novel estimators, including influence-function based estimators, for the average causal effect of a point exposure on an outcome when there are latent variables in the system

Online Causal Structure Learning in the Presence of Latent Variables

We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it does often change. Therefore, it is inappropriate to handle such changes with existing batch-learning approaches, and instead, a structure should be learned in an online manner. The online causal structure learning algorithms we present here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic and real-world datasets, the latter being a seasonally adjusted commodity price index dataset for the U.S. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.Comment: 16 pages, 9 figures, 2 table

Learning why things change: The Difference-Based Causality Learner

In this paper, we present the Difference-Based Causality Learner (DBCL), an algorithm for learning a class of discrete-time dynamic models that represents all causation across time by means of difference equations driving change in a system. We motivate this representation with real-world mechanical systems and prove DBCL's correctness for learning structure from time series data, an endeavour that is complicated by the existence of latent derivatives that have to be detected. We also prove that, under common assumptions for causal discovery, DBCL will identify the presence or absence of feedback loops, making the model more useful for predicting the effects of manipulating variables when the system is in equilibrium. We argue analytically and show empirically the advantages of DBCL over vector autoregression (VAR) and Granger causality models as well as modified forms of Bayesian and constraintbased structure discovery algorithms. Finally, we show that our algorithm can discover causal directions of alpha rhythms in human brains from EEG data