Improving public policy is one of the key roles of governments, and they can do this in an evidence-based way using administrative data. Causal inference for observational data improves on current practice of using descriptive or predictive analyses to inform policy decisions. Causal inference allows analysts to estimate the impact a policy change would have on the population if the encoded assumptions about the data generation process are valid. In this paper, we discuss the importance of causal analysis methods when analysing data to inform policy decisions. We take the education sector as a case study and provide examples of when to use a causal analysis. We use simulation to demonstrate the vital role causal diagrams play in variable selection and how bias can be introduced if extraneous variables are included in the model. Our exploration provides clear evidence for the utility of causal methods and practical examples of how to conduct such analyses. The paper promotes the incorporation of these methods in policy both for improved educational outcomes and scientific understanding.
翻译:改进公共政策是政府的主要作用之一,政府可以使用行政数据以证据为依据的方式改进公共政策。观察数据的推断改进了目前使用描述性或预测性分析为政策决定提供信息的做法。结果推断使分析家能够估计政策变化对人口的影响,如果关于数据生成过程的编码假设是有效的的话。在本文件中,我们讨论了在分析数据时因果分析方法的重要性,以便为决策提供信息。我们把教育部门作为案例研究,并举例说明何时使用因果分析。我们利用模拟来证明因果图表在变量选择中起着关键作用,如果模型中包含不相干变量,如何引入偏见。我们的探索为因果分析方法的效用和如何进行这种分析的实际例子提供了明确的证据。文件推动将这些方法纳入政策,以改进教育成果和科学理解。