Advances in Theories, Methods and Philosophy
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Paper Number
2382
Paper Type
Completed
Description
For over a decade, IS researchers have shown a great interest in examining the causal relationships between IT artifacts and a wide variety of outcomes, such as social, economic, or educational. Researchers are mainly interested in estimation of either average treatment effects, where an IT artifact causally affects an outcome. Many empirical IS studies provide only a black box view of causality and do not answer questions of “how” or “why”. Causal mediation analysis is a key to identifying critical channels and estimating the causal mediation effects enables us to uncover causal mechanisms driving the overall treatment effect. Our paper elaborates on a causal mediation framework to identify and estimate the causal mediation effects, and it provides guidance on how to use it in observational settings by applying it to estimate the direct and indirect effects of UberX on traffic congestion, with a particular focus on city bus services.
Recommended Citation
Basak, Ecem; Watson-Manheim, Mary Beth; and Tafti, Ali, "From Total Effects to Underlying Mechanisms via Causal Mediation: An Empirical Example from Ride-Hailing Platforms in the United States" (2021). ICIS 2021 Proceedings. 8.
https://aisel.aisnet.org/icis2021/adv_in_theories/adv_in_theories/8
From Total Effects to Underlying Mechanisms via Causal Mediation: An Empirical Example from Ride-Hailing Platforms in the United States
For over a decade, IS researchers have shown a great interest in examining the causal relationships between IT artifacts and a wide variety of outcomes, such as social, economic, or educational. Researchers are mainly interested in estimation of either average treatment effects, where an IT artifact causally affects an outcome. Many empirical IS studies provide only a black box view of causality and do not answer questions of “how” or “why”. Causal mediation analysis is a key to identifying critical channels and estimating the causal mediation effects enables us to uncover causal mechanisms driving the overall treatment effect. Our paper elaborates on a causal mediation framework to identify and estimate the causal mediation effects, and it provides guidance on how to use it in observational settings by applying it to estimate the direct and indirect effects of UberX on traffic congestion, with a particular focus on city bus services.
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