Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

2016

1,412 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

Citations in the corpus

• Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment (2017, positive): “When performed under an experimental setting, this is known as a randomized control trial (RCT) and is considered the gold standard for measuring the causal effects of a given treatment, under a set of conditions for a defined unit [42].”
• Causal Models for Dynamical Systems (2020, neutral): “Causal inference for longitudinal studies has been studied extensively, too [e.g. Vanderweele, 2015, Aalen et al., 2008, Robins, 1997], where the results are often formulated in the language of potential outcomes [Imbens and Rubin, 2015], rather than SCMs.”
• Characterizing Fairness Over the Set of Good Models Under Selective Labels (2021, neutral): “The joint distribution ( X i , A i , D i , Y ∗ i ) ∼ P satisfies 1) selection on observables : and 2) positivity : This assumption is common in causal inference and selection bias settings (e.g., Chapter 12 of [46] and [47]) 6 and in covariate shift learning [48].”
• Causal Inference in Accounting Research (2016, neutral): “Sometimes researchers turn to lagged 19Though some have questioned the exclusion restriction even in this case, arguing that the outcome of the draft lottery may have caused some, for example, to move to Canada (see Imbens and Rubin, 2015). 20In the case of Angrist (1990), this was plausibly satisfi”
• Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, neutral): “The potential outcome framework usually makes the following assumptions [19].”
• Human Capital Investments and Expectations about Career and Family (2016, positive): “A large econometrics and statistics literature studies how to identify these potential outcomes andmoments of the potential outcomes (e.g., average treatment effects) from realized-choice data (e.g., Heckman and Vytlacil 2005; Angrist and Pischke 2009; Imbens and Rubin 2015).”
• Evaluation of subset matching methods and forms of covariate balance (2016, neutral): “2 of [30] and Chapter 14 of [52] for related discussions).”
• Personalized Diabetes Management Using Electronic Medical Records (2016, positive): “fects are not observable, we used the weighted matching approach embedded in the kNN regression to impute potential outcomes, an approach commonly used for causal inference in observational studies when randomization is unavailable (12).”
• The Impact of Firm Prestige on Executive Compensation (2017, positive): “follow Imbens and Wooldridge (2009) and Imbens and Rubin (2015), who argue that for the purposes of matching, comparisons should be based on normalized differences, defined as”
• When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? (2019, positive): “While we begin our discussion by describing the basic linear regression model with unit fixed effects, our analysis is conducted under a more general, nonparametric setting based on the directed acyclic graphs (DAGs) and potential outcomes frameworks (Imbens and Rubin 2015; Pearl 2009).”
• The Statistics of Causal Inference: A View from Political Methodology (2015, positive): “In the Fisherian approach, there is no way to test the null hypothesis that the average effect is zero (Imbens and Rubin 2015).”
• How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, neutral): “Most methodologists consider controlled randomized experiments as the “gold” standard for causal inference (Imbens and Rubin 2015; Rosenbaum 2017).”
• Panel experiments and dynamic causal effects: A finite population perspective (2020, positive): “For T = 1, the potential outcome panel reduces to the cross-sectional potential outcome model (e.g., Holland (1986) and Imbens and Rubin (2015)).”
• Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index (2016, positive): “See Rosenbaum (1995, 2002), Rubin (2006), Morgan and Winship (2007), and Imbens and Rubin (2015), for textbook discussions and reviews of this literature.”