{"id":"W4414978137","doi":"10.1016/j.buildenv.2025.113838","title":"Human-building interaction through the lens of causality: A data-driven probabilistic causal learning approach","year":2025,"lang":"en","type":"article","venue":"Building and Environment","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Korean Canadian Scholarship Foundation; American Society of Heating, Refrigerating and Air-Conditioning Engineers","keywords":"Spurious relationship; Causal model; Probabilistic logic; Causal reasoning; Causal structure; Causality (physics); Robustness (evolution); Causal decision theory","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004862974,0.0001526665,0.0001916073,0.0000472496,0.0003388448,0.0001157733,0.0006925171,0.00006044472,0.000003282703],"category_scores_gemma":[0.00004399406,0.0001177728,0.00003255072,0.0001260986,0.0001373304,0.0003589623,0.0008969658,0.0003041666,0.000001634757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000621315,"about_ca_system_score_gemma":0.00002482524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778515,"about_ca_topic_score_gemma":0.00000250426,"domain_scores_codex":[0.9985968,0.0001425492,0.000291065,0.00054666,0.0002009904,0.0002219774],"domain_scores_gemma":[0.998997,0.0001016335,0.000126273,0.0007339676,0.00001163683,0.00002948757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001292153,0.0002951664,0.001225463,0.0002540147,0.0001752404,0.00000388462,0.002960977,0.4586223,0.01846272,0.474429,0.0004968049,0.0430615],"study_design_scores_gemma":[0.0003129616,0.0001081716,0.001854296,0.0002094134,0.00008553114,0.00001575188,0.0003014491,0.9688203,0.00069834,0.01861981,0.008687719,0.0002862771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101163,0.0002850723,0.8876026,0.0006692308,0.00009816403,0.0001512652,0.000002594064,0.00006122226,0.001013466],"genre_scores_gemma":[0.9533612,0.0001105019,0.04624618,0.0000918332,0.00003119194,0.00001882403,0.000006251048,0.000006897727,0.0001271892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8432448,"threshold_uncertainty_score":0.4802634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06669911981523967,"score_gpt":0.3062961655733281,"score_spread":0.2395970457580884,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}