Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

鉴于无数行为因素之间复杂的相互作用，包括外部世界和我们内部状态的因素，对人类行为做出因果推断是很困难的。我们对人类行为因果推断的挑战和机遇提供了非技术概念性概述。挑战包括我们模糊的因果语言和思维、统计控制不足或过度、效果异质性、干扰、效果的时间尺度和复杂的治疗方法。我们解释了为解决这些挑战之一而优化的方法如何经常加剧其他问题。因此，我们认为明确指定的研究问题是改善数据因果推断的关键。我们建议采用三角测量方法，将（准）实验研究的因果估计与观测数据和理论假设生成的因果估计进行比较。这种方法可以对理论和方法因素进行系统调查，这些因素可能导致研究中的估计趋同或发散。