arXiv subjects

Jianqiao Mao

Publications and source records attributed to Jianqiao Mao.

3 recordsLinked to original sources

arXiv2025

Front-door Reducibility: Reducing ADMGs to the Standard Front-door Setting via a Graphical Criterion

Front-door adjustment gives a simple closed-form identification formula under the classical front-door criterion, but its applicability is often viewed as narrow. By contrast, the general ID algorithm can identify many more causal effects in arbitrary graphs, yet typically outputs algebraically complex expressions that are hard to estimate and interpret. We show that many such graphs can in fact be reduced to a standard front-door setting via front-door reducibility (FDR), a graphical condition on acyclic directed mixed graphs that aggregates variables into super-nodes $(\boldsymbol{X}^{*},\boldsymbol{Y}^{*},\boldsymbol{M}^{*})$. We characterize the FDR criterion, prove it is equivalent (at the graph level) to the existence of an FDR adjustment, and present FDR-TID, an exact algorithm that finds an admissible FDR triple with correctness, completeness, and finite-termination guarantees. Empirical examples show that many graphs far outside the textbook front-door setting are FDR, yielding simple, estimable adjustments where general ID expressions would be cumbersome. FDR therefore complements existing identification methods by prioritizing interpretability and computational simplicity without sacrificing generality across mixed graphs.

stat.ML↗

arXiv2025

Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models

A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables. In high-stakes automation applications of ML this is problematic, as the model often learns spurious, non-causal associations. This paper proposes mechanism learning, a simple method which uses causally weighted Gaussian Mixture Models (CW-GMMs) to deconfound observational data such that any appropriate ML model is forced to learn predictive relationships between effects and their causes (reverse causal inference), despite the potential presence of multiple unknown and unmeasured confounding. Effect variables can be very high-dimensional, and the predictive relationship nonlinear, as is common in ML applications. This novel method is widely applicable, the only requirement is the existence of a set of mechanism variables mediating the cause (prediction target) and effect (feature data), which is independent of the (unmeasured) confounding variables. We test our method on fully synthetic, semi-synthetic and real-world datasets, demonstrating that it can discover reliable, unbiased, causal ML predictors where by contrast, the same ML predictor trained naively using classical supervised learning on the original observational data, is heavily biased by spurious associations. We provide code to implement the results in the paper, online.

cs.LG↗

arXiv2021

Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic Buildings

An ensuing challenge in Artificial Intelligence (AI) is the perceived difficulty in interpreting sophisticated machine learning models, whose ever-increasing complexity makes it hard for such models to be understood, trusted and thus accepted by human beings. The lack, if not complete absence, of interpretability for these so-called black-box models can lead to serious economic and ethical consequences, thereby hindering the development and deployment of AI in wider fields, particularly in those involving critical and regulatory applications. Yet, the building services industry is a highly-regulated domain requiring transparency and decision-making processes that can be understood and trusted by humans. To this end, the design and implementation of autonomous Heating, Ventilation and Air Conditioning systems for the automatic but concurrently interpretable optimisation of energy efficiency and room thermal comfort is of topical interest. This work therefore presents an interpretable machine learning model aimed at predicting room temperature in non-domestic buildings, for the purpose of optimising the use of the installed HVAC system. We demonstrate experimentally that the proposed model can accurately forecast room temperatures eight hours ahead in real-time by taking into account historical RT information, as well as additional environmental and time-series features. In this paper, an enhanced feature engineering process is conducted based on the Exploratory Data Analysis results. Furthermore, beyond the commonly used Interpretable Machine Learning techniques, we propose a Permutation Feature-based Frequency Response Analysis (PF-FRA) method for quantifying the contributions of the different predictors in the frequency domain. Based on the generated reason codes, we find that the historical RT feature is the dominant factor that has most impact on the model prediction.

cs.LG↗