{"id":"W2752178302","doi":"10.5465/ambpp.2017.187","title":"Causal Modeling in HR Analytics: A Practical Guide to Models, Pitfalls, and Suggestions","year":2017,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Analytics; Computer science; Data science; Management science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006112014,0.000200991,0.0002725043,0.0006119912,0.000281973,0.0004083043,0.0005941322,0.0001474102,0.00001014223],"category_scores_gemma":[0.0003045943,0.0001994749,0.0000402822,0.000242471,0.0001051717,0.002745076,0.001331901,0.0002800599,0.00002363761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003288824,"about_ca_system_score_gemma":0.000005161697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002566703,"about_ca_topic_score_gemma":0.00002062689,"domain_scores_codex":[0.9984754,0.000001252649,0.0004031771,0.0004424251,0.0003094082,0.0003682852],"domain_scores_gemma":[0.9994654,0.00001338582,0.0002682548,0.0001478945,0.00008220142,0.00002288087],"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":[0.0000446451,0.00008113077,0.006982754,0.0004288588,0.00005612776,0.000007179337,0.00006428587,0.0009627045,0.00007258198,0.9684496,0.01578211,0.007068032],"study_design_scores_gemma":[0.001334289,0.0000313175,0.02245557,0.000623072,0.0003190752,0.000004620636,0.002783073,0.7162696,0.00007366916,0.198339,0.05700011,0.0007666508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7689722,0.0001490205,0.009371776,0.07970496,0.00008566774,0.001140615,0.00000260347,0.0004134676,0.1401596],"genre_scores_gemma":[0.9880739,0.0002509718,0.009728385,0.001136995,0.0001470501,0.0000469736,0.000001257384,0.00002391326,0.0005904911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7701106,"threshold_uncertainty_score":0.813435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08306206643484328,"score_gpt":0.326359794580504,"score_spread":0.2432977281456607,"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."}}