Harnessing heterogeneity in behavioural research using computational social science

IF 5.1 Q1 PSYCHOLOGY, APPLIED Behavioural Public Policy Pub Date : 2023-12-04 DOI:10.1017/bpp.2023.35
Giuseppe A. Veltri
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Abstract

Similarly to other domains of the social sciences, behavioural science has grappled with a crisis concerning the effect sizes of research findings. Different solutions have been provided to answer this challenge. This paper will discuss analytical strategies developed in the context of computational social science, namely causal tree and forest, that will benefit behavioural scientists in harnessing heterogeneity of treatment effects in RCTs. As a mixture of theoretical and data-driven approaches, these techniques are well suited to exploit the rich information provided by large studies conducted using RCTs. We discuss the characteristics of these methods and their methodological rationale and provide simulations to illustrate their use. We simulate two scenarios of RCTs-generated data and explore the heterogeneity of treatment effects using causal tree and causal forest methods. Furthermore, we outlined a potential theoretical use of these techniques to enrich behavioural science ecological validity by introducing the notion of behavioural niche.
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在行为研究中利用计算社会科学的异质性
与社会科学的其他领域类似,行为科学也面临着研究结果效应大小的危机。已经提供了不同的解决方案来应对这一挑战。本文将讨论在计算社会科学背景下开发的分析策略,即因果树和森林,这将有利于行为科学家利用随机对照试验中治疗效果的异质性。作为理论和数据驱动方法的混合,这些技术非常适合利用使用随机对照试验进行的大型研究提供的丰富信息。我们讨论了这些方法的特点和它们的方法原理,并提供模拟来说明它们的使用。我们模拟了rct生成数据的两种情况,并使用因果树和因果森林方法探索治疗效果的异质性。此外,我们概述了这些技术的潜在理论用途,通过引入行为生态位的概念来丰富行为科学的生态有效性。
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CiteScore
7.90
自引率
2.00%
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0
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