Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry

2023-09-11 · Xia Chen, Ruiji Sun, Ueli Saluz, Stefano Schiavon, Philipp Geyer

The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models. We investigated the synergetic pattern between the data-driven methods, empirical domain knowledge, and first-principles simulations. We showed the potential risk of biased results when using data-driven models without causal analysis. Using a case study assessing the implication of several design solutions on the energy consumption of a building, we proved the necessity of causal analysis during the data-driven modeling process. We concluded that: (a) Data-driven models' accuracy assessment or domain knowledge screening may not rule out biased and spurious results; (b) Data-driven models' feature selection should involve careful consideration of causal relationships, especially colliders; (c) Causal analysis results can be used as an aid to first-principles simulation design and parameter checking to avoid cognitive biases. We proved the benefits of causal analysis when applied to data-driven models in building engineering.

📄 PDF Abstract BibTeX arXiv:2309.11509

Code (0)

등록된 구현이 없습니다.

Tasks

Causal Inference Decision Makingfeature selection

Methods 이 논문이 사용한 방법론

Feature Selection Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables,…

Robust Weighted Triangulation of Causal Effects Under Model Uncertainty

2026-03-01 · Rohit Bhattacharya, Ina Ocelli, Ted Westling arxiv

A fundamental challenge in causal inference with observational data is correct specification of a causal model. When there is model uncertainty, analysts may seek to use estimates from multiple candidate models that rely…

Causal Inference

Generalized Bayes for Causal Inference

2026-03-03 · Emil Javurek, Dennis Frauen, Yuxin Wang, Stefan Feuerriegel arxiv

Uncertainty quantification is central to many applications of causal machine learning, yet principled Bayesian inference for causal effects remains challenging. Standard Bayesian approaches typically require specifying a…

Bayesian Inference Causal Inference

CauScale: Neural Causal Discovery at Scale

2026-02-09 · Bo Peng, Sirui Chen, Jiaguo Tian, Yu Qiao 외 arxiv

Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs. To add…

Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation

2026-05-06 · Olivia Jullian Parra, Sara Zoccheddu, David Catalan Cerezo, Tom Forzy 외 arxiv

Target trial emulation (TTE) enables causal questions to be studied with observational data when randomized controlled trials (RCTs) are infeasible. Yet treatment-effect methods often address causal estimation, missingne…

Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation

2023-03-02 · Damian Machlanski, Spyridon Samothrakis, Paul Clarke

The performance of most causal effect estimators relies on accurate predictions of high-dimensional non-linear functions of the observed data. The remarkable flexibility of modern Machine Learning (ML) methods is perfect…

Causal Inference Model Selection