Nicole E Pashley
Nicole E Pashley
This is a review of Peng Ding's textbook "A First Course in Causal Inference." The book builds causal inference topics up from basics in experiments to complex observational studies. This review discusses the book's style and content as wel...
Peng Ding
Peng Ding
Aronow et al. (2024) provide a great service to the causal inference community by delineating the key results in Robins and Ritov (1997). They show that randomized controlled trials (RCTs) ensure much stronger statistical inference than unc...
Commentary on ``Nonparametric identification is not enough, but randomized controlled trials are'': Statistical considerations for generating reliable evidence across a spectrum of studies that increasingly involve real-world elements [0.03%]
对《非参数识别不够，但随机对照试验足够》的评论：在日益涉及现实世界要素的研究范围中产生可靠证据的统计考量
Rachael Phillips,Mark van der Laan
Rachael Phillips
Judea Pearl, quoted in Pearl and Mackenzie (2008), stated that "once we have understood why [randomized controlled trials] RCTs work, there is no need to put them on a pedestal and treat them as the gold standard of causal analysis, which a...
Benjamin Recht
Benjamin Recht
This commentary proposes a framework for understanding the role of statistics in policymaking, regulation, and bureaucratic systems. I introduce the concept of "ex ante policy," describing statistical rules and procedures designed before da...
Why are RCTs the Gold Standard? The Epistemological Difference Between Randomized Experiments and Observational Studies [0.03%]
为何随机对照试验是金标准？随机实验与观察研究的认识论差异
Christopher Harshaw
Christopher Harshaw
In response to Pearl, Aronow et al. (2025) argue that randomized experiments are special among causal inference methods due to their statistical properties. I believe that the key distinction between randomized experiments and observational...
Arman Oganisian,Antonio Linero
Arman Oganisian
Aronow et al. (2025) provide a convincing case for the special status of randomized controlled trials (RCTs) in which the propensity scores are known and can be used to make causal inferences. Here we provide a Bayesian perspective on their...
Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are [0.03%]
回应评论：“非参数识别不够，但随机对照试验足够”
P M Aronow,James M Robins,Theo Saarinen et al.
P M Aronow et al.
We thank the editor for organizing a diverse and wide-ranging discussion, and we thank the commentators for their detailed and thoughtful remarks. Most of the commentators provide broader perspectives on randomized experiments and their rol...
Nonparametric identification is not enough, but randomized controlled trials are [0.03%]
非参数识别是不够的，但随机对照试验可以补上
P M Aronow,James M Robins,Theo Saarinen et al.
P M Aronow et al.
We argue that randomized controlled trials (RCTs) are special even among studies for which a nonparametric unconfoundedness assumption is credible. This claim follows from two results of Robins and Ritov (1997). First, in settings with at l...
Drew Dimmery,Kevin Munger
Drew Dimmery
We provide a critical response to Aronow et al. (2021) which argued that randomized controlled trials (RCTs) are "enough," while nonparametric identification in observational studies is not. We first investigate what is meant by "enough," a...
Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes [0.03%]
利用试点数据进行观测研究的功效分析以估计动态治疗方案
Eric J Rose,Erica E M Moodie,Susan Shortreed
Eric J Rose
Significant attention has been given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this approach through a sequence of decision rules that map p...