Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”

2003 ◽  
Vol 28 (4) ◽  
pp. 353-368 ◽  
Author(s):  
Junni L. Zhang ◽  
Donald B. Rubin
Keyword(s):  
Sample Space ◽  
Causal Effects ◽  
Outcome Variable ◽  
Principal Stratification ◽  
Randomized Experiments ◽  
Large Sample ◽  
Traditional Approaches

The topic of “truncation by death” in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither “censored” nor “missing,” but should be treated as being defined on an extended sample space. Using an educational example to illustrate, we will outline here a formulation for tackling this issue, where we call the outcome “truncated by death” because there is no hidden value of the outcome variable masked by the truncating event. We first formulate the principal stratification ( Frangakis & Rubin, 2002 ) approach, and we then derive large sample bounds for causal effects within the principal strata, with or without various identification assumptions. Extensions are then briefly discussed.

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.

The bidirectional association between Internet addiction and depression: a large-sample longitudinal study among Chinese university students (Preprint)

2020 ◽  
Author(s):  
Wan-Jun Guo ◽  
Xia Yang ◽  
Yu-Jie Tao ◽  
Ya-Jing Meng ◽  
Hui-Yao Wang ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Internet Addiction ◽  
Causal Effects ◽  
Causal Relationships ◽  
Depression Severity ◽  
Large Sample ◽  
Panel Model ◽  
Bidirectional Association ◽  
Predictive Effect

BACKGROUND Evidence indicates that Internet addiction (IA) is associated with depression, but longitudinal studies have rarely been reported, and no studies have yet investigated potential common vulnerability or a possible specific causal relationship between these disorders. OBJECTIVE To overcome these gaps, the present 12-month longitudinal study based on a large-sample employed a cross-lagged panel model (CLPM) approach to investigate the potential common vulnerability and specific cross-causal relationships between IA and CSD (or depression). METHODS IA and clinically-significant depression (CSD) among 12 043 undergraduates were surveyed at baseline (as freshmen) and in follow-up after 12 months (as sophomores). Application of CLPM revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. RESULTS Rates of baseline IA and CSD were 5.47% and 3.85%, respectively; increasing to 9.47% and 5.58%, respectively at follow-up. Among those with baseline IA and CSD, 44.61% and 34.48% remained stable at the time of the follow-up survey, respectively. Rates of new-incidences of IA and CSD over 12 months were 7.43% and 4.47%, respectively. Application of a cross-lagged panel model approach (CLPM, a discrete time structural equation model used primarily to assess causal relationships in real-world settings) revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. Models revealed that baseline CSD (or depression severity) had a significant net-predictive effect on follow-up IA (or IA severity), and baseline IA (or IA severity) had a significant net-predictive effect on follow-up CSD (or depression severity). CONCLUSIONS These correlational patterns using a CLPM indicate that both common vulnerability and bidirectional specific cross-causal effects between them may contribute to the association between IA and depression. As the path coefficients of the net-cross-predictive effects were significantly smaller than those of baseline to follow-up cross-section associations, vulnerability may play a more significant role than bidirectional-causal effects. CLINICALTRIAL Ethics Committee of West China Hospital, Sichuan University (NO. 2016-171)

scholarly journals Causal Effects, Migration and Legacy Studies

2021 ◽  
Author(s):  
Moritz Marbach
Keyword(s):  
Civil Rights ◽  
Exposure Time ◽  
Causal Effects ◽  
Movement Behavior ◽  
Attitudinal Change ◽  
Principal Stratification ◽  
Legacy Effects ◽  
Social Scientists ◽  
Attitudinal Differences ◽  
Formal Framework

Social scientists have long been interested in the persistent effects of history on contemporary behavior and attitudes. To estimate legacy effects, studies typically compare people living in places that were historically exposed to some event and those that were not. Using principal stratification, we provide a formal framework to analyze how migration limits our ability to learn about the persistent effects of history from observed differences between historically exposed and unexposed places. We state the necessary assumptions about movement behavior to causally identify legacy effects. We highlight that these assumptions are strong; therefore, we recommend that legacy studies circumvent bias by collecting data on people's place of residence at the exposure time. Reexamining a study on the persistent effects of US civil-rights protests, we show that observed attitudinal differences between residents and non-residents of historic protest sites are more likely due to migration rather than attitudinal change.

Will Magnetic Properties near Pressure Induced Quantum Critical Phenomena Be Elucidated?

2021 ◽  
Vol 1 ◽  
Author(s):  
Yoshiya Uwatoko ◽  
Keyword(s):  
Magnetic Properties ◽  
Critical Phenomena ◽  
Sample Space ◽  
Magnetization Measurements ◽  
Large Sample ◽  
Pressure Cell ◽  
Quantum Critical Phenomena ◽  
Quantum Critical ◽  
Type Pressure

Hiraoka et al. developed an opposed anvil-type pressure cell for magnetization measurements which combines a large sample space and is capable of generating higher pressures up to 6.3 GPa.

Don’t Throw Your Heart Away: Increased Transparency of Donor Utilization Practices in Transplant Center Report Cards Alters How Center Performance Is Evaluated

2021 ◽  
pp. 0272989X2110389
Author(s):  
Alison E. Butler ◽  
Gretchen B. Chapman
Keyword(s):  
Survival Rates ◽  
Report Cards ◽  
Outcome Variable ◽  
Practical Reasons ◽  
Randomized Experiments ◽  
Medical Trainees ◽  
Center Report ◽  
Outcome Information ◽  
Hypothetical Scenario ◽  
Hospital Report Cards

Background Publicly available report cards for transplant centers emphasize posttransplant survival and obscure the fact that some centers reject many of the donor organs they are offered (reflecting a conservative donor acceptance strategy), while others accept a broader range of donor offers (reflecting an open donor acceptance strategy). Objective We assessed how the provision of salient information about donor acceptance practices and waitlist survival rates affected evaluation judgments of hospital report cards given by laypeople and medical trainees. Methods We tested 5 different report card formats across 4 online randomized experiments ( n1 = 1,003, n2 = 105, n3 = 123, n4 = 807) in the same hypothetical decision. The primary outcome variable was a binary choice between transplant hospitals (one with an open donor acceptance strategy and the other with a conservative donor acceptance strategy). Results Report cards featuring salient information about donor organ utilization rates (transplant outcomes categorized by quality of donor offers accepted) or overall survival rates (outcomes from both waitlist and transplanted patients) led lay participants (studies 1, 3, and 4) and medical trainees (study 2) to evaluate transplant centers with open donor acceptance strategies more favorably than centers with conservative strategies. Limitations Due to the nature of the decision, a hypothetical scenario was necessary for both ethical and practical reasons. Results may not generalize to transplant clinicians or patients faced with the decision of where to join the transplant waitlist. Conclusions These findings suggest that performance evaluations for transplant centers may vary significantly based not only on what outcome information is presented in report cards but also how the information is displayed.

The future of an allusion

2011 ◽  
Vol 1 (1) ◽  
pp. 173-181
Author(s):  
Gerald C. Cupchik
Keyword(s):  
Everyday Life ◽  
Natural World ◽  
Sample Space ◽  
Literary Texts ◽  
Francis Bacon ◽  
Task Set ◽  
Large Sample ◽  
Careful Observation ◽  
Natural Processes ◽  
Underlying Processes

How shall we guide ourselves to ensure that research on creation and reception are ecologically valid, representative of natural processes in everyday life? Rather than romanticizing science, we need to be critically aware of the hidden assumptions which lie behind the empirical narratives that appear in journals. Francis Bacon awakened us to the “false idols” and Goethe encouraged us to place the careful observation of phenomena of the natural world ahead of theories which should be derived from observation and help focus our understanding of them. Following Bacon and Goethe, I advise that researchers should work from actual instances or episodes in which the phenomena of interest are manifested. Our goal should be to ensure that the parallel world of experimentation is not divorced from everyday life. This can be achieved by developing a large sample space of materials (poems, literary texts, films, and so forth) from which at least two must be chosen to represent each kind of stimulus. Further, we need to specify the orienting task set according to which participants are to approach these materials. Finally, I advocate the use of quantitative and qualitative kinds of data in a complementary manner to elucidate the underlying processes. By engaging in an empirical dialogue with ourselves and others, we can advance our understanding and explanation of the phenomena which fascinate us.

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