Experimental designs for identifying causal mechanisms

By Kosuke Imai, Dustin Tingley, Teppei Yamamoto. 2013

410 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Education, Statistics and Probability

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “, Imai et al. [2013], Peters et al. [2016]); the selection of measurement readouts, time points, or intervention strate gies could guide experimentation and has the potential to significantly reduce the number of complicated and expensive experiments.”
Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables (2014, background): “This is equally attractive as directly manipulating the mediator exogenously; see the discussion in Imai et al. (2013).”
A Minimax Surrogate Loss Approach to Conditional Difference Estimation (2018, background): “Recently there has been a lot of work on subgroup identification [16, 18, 25] and other types of nonlinear predictors of causal effects [3, 19, 2, 36, 34].”
Explaining Group Influence: The Role of Identity and Emotion in Political Conformity and Polarization (2015, result): “Second, group influence is examined However, one could argue that—taken together—the two studies test mediation: Group norms arouse self-conscious emotions in Study 1, and their causal effects are demonstrated in Study 2 (see Bullock, Green, and Ha 2010; Imai, Tingley, and Yamamoto 2012).”
IDENTIFYING CAUSAL MECHANISMS (PRIMARILY) BASED ON INVERSE PROBABILITY WEIGHTING (2014, background): “Unfortunately, this assumption appears unattractive in empirical applications (see, for instance, the discussion in Imai et al., 2012, Section 3.1) and restricts the usefulness of nonparametric identification advocated in recent work.”
Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects (2016, background): “As Imai, Keele, and Yamamoto (2010) point out, no intermediate confounders “is an important lim- 7 Imai and Yamamoto (2013) present a method for causal mediation in the face of intermediate confounders, which they refer to as multiple causal mechanisms.”
Causal Mediation Analysis (2011, methodology): “Alternatively, we suggest conducting sensitivity analyses, which we show how to do using our package in section 4 or adopting different experimental designs (Imai et al. 2009).”
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments (2013, methodology): “In this section, we extend our method to the two new experimental designs recently proposed by Imai, Tingley, and Yamamoto (2013), where the mediator is manipulated to avoid the exogeneity assumption.”
Quo Vadis, Dynamic Capabilities? A Content-Analytic Review of the Current State of Knowledge and Recommendations for Future Research (2018, background): “, Fischer, Gebauer, Gregory, Ren, & Fleisch, 2010; Jenkins, 2010). These studies suggest that the development of dynamic capabilities may proceed through a series of typical stages, knowledge of which may prove particularly useful for practitioners attempting to implement such capabilities. Process-”
Meaningful Mediation Analysis: Plausible Causal Inference and Informative Communication (2017, methodology): “Tailored designs to implement experimental mediation analysis have been proposed (Imai, Tingley, and Yamamoto 2013).”