因果推論在行為科學研究中占有重要地位，常用於回答關於人類、社會和市場行為的因果問題。傳統因果推論方法包括統計學或計量經濟學的研究設計與模型。近年來，Pearl（2000）則引入了因果圖和結構化因果模型作為因果推斷的工具。因果圖以圖像化的方式來直觀地呈現因果關係，使用節點和箭頭來描述研究設計、研究人員心中的因果假設，幫助研究人員得出因果推論。在流行病學和生物醫學研究中，因果圖常被推廣、應用於因果推論研究。此外，因果圖也被推廣於資訊系統（IS）實驗研究，因為在進行數據分析時，它能指引研究員如何正確地控制變數，也幫助解決實驗執行複雜性 (complications) 造成的分析問題或協助進行次群體分析。在人機互動（HCI）領域，研究人員常好奇某個科技物品對人、事、物帶來的因果影響；而為了回答這些因果問題，研究人員常使用「隨機實驗」此研究設計。儘管因果圖能夠幫助因果推論進行，但它尚不常被應用於 HCI 領域的實驗研究中。在本論文中，我們旨在向 HCI 研究人員介紹因果圖和 Tafti & Shmueli（2020）的因果圖應用框架，以便研究人員能夠利用因果圖進行隨機實驗。我們首先簡要介紹因果圖、隨機實驗以及因果圖如何應用於隨機實驗。然後，我們找出 HCI 中最常見的隨機實驗設計；對於三種常見的隨機實驗設計，我們展示了如何將每種研究設計轉化為因果圖，以及這些因果圖如何幫助研究人員進行實驗並得出因果推論。我們也將展示因果圖如何協助處理實驗複雜性造成的分析難題，並從收回來的資料去找出還有哪些可進行推測的因果關係。最後，我們討論了關於因果圖的限制。

Causal inference holds a prominent position in behavioral sciences research, where it is used to answer causal questions about behaviors of humans, societies, and markets. Traditional methods for causal inference include statistical and econometric study designs and models. More recently, Pearl (2000) introduced causal diagrams and structural causal modeling as a useful tool for causal inference. A causal diagram is a graphical depiction of causal relationships, using nodes and arrows to visually represent research designs and assumptions about causal relationships, thereby aiding researchers in drawing causal inferences. Causal diagrams have been widely promoted and utilized in epidemiology and biomedical research to aid causal inference. Additionally, they have been promoted for information system (IS) experimental studies because they instruct the correct way of conditioning on variables during data analysis, helping to address complications or perform subgroup analyses. In the field of Human-Computer Interaction (HCI), researchers aim to understand the causal effect of an artifact on an outcome of interest. To address such causal questions, randomized experiments are a popular research design choice. Despite the many benefits of causal diagrams for causal inference, their application in guiding causal inference and conducting experiments in the HCI field has been limited. In this thesis, we aim to introduce causal diagrams to HCI researchers and the framework by Tafti & Shmueli (2020) so researchers can benefit from using causal diagrams in conducting randomized experiments. We start by providing a brief introduction to causal diagrams, randomized experiments, and how causal diagrams can be used in randomized experiments. We then identify the most common experimental designs in HCI. For three prominent study designs, we demonstrate how each study design can be translated into a causal diagram, and how the resulting causal diagrams can be used by researchers to help them conduct experiments and make causal inferences. We demonstrate how causal diagrams can be useful for representing and dealing with experimental complications, identifying further causal effects beyond the main effect of interest. We also show some limitations of causal diagrams.

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