Causal Inference in Block-Randomized Experiments: Analysis Based on Neyman’s Stochastic Causal Model

2014 ◽  
Vol 3 (6) ◽  
pp. 184
Author(s):  
Emil Scosyrev
Keyword(s):  
Causal Inference ◽  
Causal Model ◽  
Randomized Experiments

scholarly journals The Inflation Technique for Causal Inference with Latent Variables

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Elie Wolfe ◽  
Robert W. Spekkens ◽  
Tobias Fritz
Keyword(s):  
Probability Distribution ◽  
Causal Inference ◽  
Latent Variables ◽  
Causal Model ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Difficult Case ◽  
Common Causes ◽  
Multiple Copies ◽  
New Inequalities

AbstractThe problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each of the original variables, but where the ancestry of each copy mirrors that of the original. To every distribution of the observed variables that is compatible with the original causal structure, we assign a family of marginal distributions on certain subsets of the copies that are compatible with the inflated causal structure. It follows that compatibility constraints for the inflation can be translated into compatibility constraints for the original causal structure. Even if the constraints at the level of inflation are weak, such as observable statistical independences implied by disjoint causal ancestry, the translated constraints can be strong. We apply this method to derive new inequalities whose violation by a distribution witnesses that distribution’s incompatibility with the causal structure (of which Bell inequalities and Pearl’s instrumental inequality are prominent examples). We describe an algorithm for deriving all such inequalities for the original causal structure that follow from ancestral independences in the inflation. For three observed binary variables with pairwise common causes, it yields inequalities that are stronger in at least some aspects than those obtainable by existing methods. We also describe an algorithm that derives a weaker set of inequalities but is more efficient. Finally, we discuss which inflations are such that the inequalities one obtains from them remain valid even for quantum (and post-quantum) generalizations of the notion of a causal model.

scholarly journals Improving causal inference of mediation analysis with multiple mediators in randomized experiments

2021 ◽  
Author(s):  
Wen Wei Loh ◽  
Dongning Ren
Keyword(s):  
Causal Inference ◽  
Mediation Analysis ◽  
Causal Structure ◽  
Psychology Research ◽  
Direct Effects ◽  
Randomized Experiments ◽  
Causal Inferences ◽  
Mediated Effects ◽  
Causal Assumptions ◽  
Intermediate Variables

Mediation analysis is an essential tool for investigating how a treatment causally affects an outcome via intermediate variables. However, violations of the (often implicit) causal assumptions can severely threaten the validity of causal inferences of mediation analysis. Psychologists have recently started to raise such concerns, but the discussions have been limited to mediation analysis with a single mediator. In this article, we examine the causal assumptions when there are multiple possible mediators. We pay particular attention to the practice of exploring mediated effects along various paths linking several mediators. Substantive conclusions using such methods are predicated on stringent assumptions about the underlying causal structure that can be indefensible in practice. Therefore, we recommend that researchers shift focus to mediator-specific indirect effects using a recently proposed framework of interventional (in)direct effects. A vital benefit of this approach is that valid causal inference of mediation analysis with multiple mediators does not necessitate correctly assuming the underlying causal structure among the mediators. Finally, we provide a practical guide with suggestions to improve the research practice of mediation analysis at each study stage. We hope this article will encourage explication, justification, and reflection of the causal assumptions underpinning mediation analysis to improve the validity of causal inferences in psychology research.

Causation and Explanation

2010 ◽  
Author(s):  
Peter Lipton
Keyword(s):  
Causal Inference ◽  
Causal Model ◽  
The Third ◽  
The Relationship

In its simplest form, a causal model of explanation maintains that to explain some phenomenon is to give some information about its causes. This prompts four questions that will structure the discussion to follow. The first is whether all explanations are causal. The second is whether all causes are explanatory. The answer to both of these questions turns out to be negative, and seeing why this is so helps to clarify the relationship between causation and explanation. The third question is itself a request for an explanation: Why do causes explain, when they do? Why, for example, do causes explain their effects but effects not explain their causes? Finally, the article considers how explanation can illuminate the process of causal inference.

scholarly journals Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

2018 ◽  
Vol 48 (1) ◽  
pp. 136-151 ◽  
Author(s):  
Guillaume W. Basse ◽  
Edoardo M. Airoldi
Keyword(s):  
Causal Inference ◽  
Nonparametric Inference ◽  
Causal Effects ◽  
Randomized Experiments ◽  
Interference Structure ◽  
Large Sample ◽  
Unbiased Estimators ◽  
Nonparametric Approach ◽  
Large Samples ◽  
Interference Mechanism

Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies. This reliance on explicit restrictions on the interference mechanism suggests a shared intuition that inference is impossible without any assumptions on the interference structure. In this paper, we begin by formalizing this intuition in the context of a classical nonparametric approach to inference, referred to as design-based inference of causal effects. Next, we show how, always in the context of design-based inference, even parametric structural assumptions that allow the existence of unbiased estimators cannot guarantee a decreasing variance even in the large sample limit. This lack of concentration in large samples is often observed empirically, in randomized experiments in which interference of some form is expected to be present. This result has direct consequences for the design and analysis of large experiments—for instance, in online social platforms—where the belief is that large sample sizes automatically guarantee small variance. More broadly, our results suggest that although strategies for causal inference in the presence of interference borrow their formalism and main concepts from the traditional causal inference literature, much of the intuition from the no-interference case do not easily transfer to the interference setting.

scholarly journals Methods of causal inference in contemporary political science

2021 ◽  
pp. 98-115
Author(s):  
Yevgeniy Sedashov
Keyword(s):  
Causal Inference ◽  
Political Science ◽  
Research Design ◽  
Research Problem ◽  
General Description ◽  
Randomized Experiments ◽  
Design Algorithm ◽  
Advantages And Disadvantages ◽  
Quantitative And Qualitative Research ◽  
Inference Methods

This paper serves as an exposition of the causal inference methods that are most popular in political science. Rather than focusing on technical details we present a brief summary of main ideas behind each method with the goal of making them accessible to a broad audience of researchers. We also provide a research design algorithm for each method. First, we focus on a general motivation behind causal inference methods. We discuss how the problem of causality arises in hypothesis testing and describe the relationship between democracy and economic development as a case in point. Second, we give an exposition of a general causality problem within the framework of Rubin Causal Model (RCM). We provide all basic definitions and then demonstrate how the problem of causal inference arise within RCM. Third, we describe the most frequently used methods of causal inference such as randomized experiments, regression discontinuity design, difference-in-difference design, and instrumental variables. For each method we give a reader a general description as well as steps of a research design. We also briefly discuss advantages and disadvantages of each method. Armed with this knowledge, a reader can use it to find the method that is the most appropriate for a research problem at hand. We conclude by arguing that the ideas of causal inference are useful for both quantitative and qualitative research.

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