Causal Inference in Randomized Experiments

2009 ◽  
pp. 21-63 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Causal Inference ◽  
Randomized Experiments

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.

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.

scholarly journals A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

2021 ◽  
Author(s):  
Alice J. Sommer ◽  
Annette Peters ◽  
Josef Cyrys ◽  
Harald Grallert ◽  
Dirk Haller ◽  
...  
Keyword(s):  
Causal Inference ◽  
Environmental Exposure ◽  
Gut Microbiome ◽  
Smoking Prevention ◽  
Randomized Experiments ◽  
Human Gut ◽  
Microbial Network ◽  
Starting Point ◽  
Using Data ◽  
Microbiome Data

AbstractStatistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. The observational character of prospective cohort data and the intricate characteristics of microbiome data make it, however, challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

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