Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
A Comparison of Ensemble Learning Algorithms for Matching Weights Method: A Simulation Study
Eşleştirilmiş Ağırlıklandırılma Yöntem İçin Topluluk Öğrenme Algoritmalarının Karşılaştırması: Bir Simülasyon Çalışması
Hülya KOÇYİĞİTa
aDepartment of Mathematics and Science Education, Karamanoğlu Mehmetbey University Faculty of Education, Karaman, Türkiye
Turkiye Klinikleri J Biostat. 2023;15(1):30-41
doi: 10.5336/biostatic.2022-94304
Article Language: EN
Full Text
ABSTRACT
 Objective: This paper conducts thorough simulation research to assess the effectiveness of ensemble learning techniques and logistics regression models for estimating propensity score values used at the matching weighting under different propensity score model scenarios and various treatment scenarios considered. Material and Methods: This study underlines the significance and challenges of frequently disregarded overlap assumption. Offered method also is examined and focuses on the difficulties that nonoverlap entails for inference. Monte Carlo simulations are used to generate data sets to analyze the causal effect of meeting in order that illustrates alternative strategies and pertaining aspects when highlighting positivity violations. Results: Here simulation results are illustrated to compare matching weight method under various machine learning methods in terms of root mean squared error (RMSE), SE of the treatment effects, and bias. Some ensemble learning algorithms for estimating propensity score (PS) values have rigorously outperformed than using the logistics regression method with or without existing a violation of the positivity the assumption under the different estimation PS models and various treatment models. The most complex treatment scenario tends to produce better results as measured by the SE, RMSE and bias than the less complex treatment scenarios. Conclusion: The findings summarize the conditions under which one technique may be anticipated to perform better than others without generalizing whether a method is always preferable to the other.

Keywords: Matching weighting; observational studies; ensemble learning models; propensity score; Monte Carlo simulation
ÖZET
 Amaç: Bu makalede dikkate alınan farklı eğilim puanı modeli senaryoları ve çeşitli tedavi senaryoları altında, eşleştirme ağırlıklandırılması metodunun kullanılan tahmini eğilim puan değerleri hesaplanması için topluluk öğrenme teknikleri ve lojistik regresyon modelinin etkinliğinin değerlendirmek için kapsamlı bir simülasyon çalışması yürütmektedir. Gereç ve Yöntemler: Bu çalışma, sıklıkla ihmal edilen örtüşme varsayımının önemini ve zorlukları vurgulamaktadır. Önerilen yöntemin de değerlendirilmesi ve nedensel çıkarımlarda örtüşmeme durumunun getirdiği zorluklara odaklanmıştır. Pozitiflik varsayımını vurgulanmasında alternatif stratejileri ve ilgili yönleri tanımlamak için nedensel etkiyi analiz etmek için Monte Carlo simülasyonundan elde edilen veri setleri kullanılır. Bulgular: Buradaki simülasyon çalışmasının sonuçları, farklı makine öğrenimi yöntemleri altında eşleştirme ağırlıklandırılması metodunun kök ortalama kare hatası [root mean squared error (RMSE)], tedavi etkilerinin SE ve göreceli ön yargı ölçülerine dayalı karşılaştırma yapmak için gösterilmektedir. Farklı tahmin eğilim skoru [propensity score (PS)] modelleri ve birçok tedavi modelleri altından pozitiflik varsayımının ihlali olsun veya olmasın PS değerlerinin tahmin etmek için kullanılan bazı topluluk öğrenme algoritmalarının, lojistik regresyon yönteminin kullanılmasından kesinlikle daha iyi performans göstermiştir. En karmaşık tedavi senaryosu, SE, RMSE ve yanlılıkla ölçüleri açısından daha az karmaşık tedavi senaryolarına göre daha iyi sonuçlar üretme eğilimindedir. Sonuç: Bulgular kısmıyla bir yöntemin diğerinden daha iyidir genelleştirmesini yapmadan, bir tekniğin diğerlerinden daha iyi bir performans göstermesinin beklenebileceğinin şartlarıyla özetlenmektedir.

Anahtar Kelimeler: Eşleştirme ağırlıklandırılması; gözlemsel çalışmalar; topluluk öğrenme modeller; eğilim skoru; Monte Carlo simülasyon
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