Causal Inference Struggles with Agency on Online Platforms

Author:

Milli Smitha1,Belli Luca2,Hardt Moritz3

Affiliation:

1. University of California, Berkeley, USA

2. Twitter, USA

3. Max Planck Institute for Intelligent Systems, Germany

Publisher

ACM

Reference45 articles.

1. Julia Angwin , Jeff Larson , Surya Battu , and Lauren Kirchner . 2016. Machine Bias. ProPublica ( 2016 ). Julia Angwin, Jeff Larson, Surya Battu, and Lauren Kirchner. 2016. Machine Bias. ProPublica (2016).

2. Comparing Experimental and Matching Methods Using a Large-Scale Voter Mobilization Experiment

3. Peter C Austin and Elizabeth A Stuart. 2015. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in medicine 34 28 (2015) 3661–3679. Peter C Austin and Elizabeth A Stuart. 2015. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in medicine 34 28 (2015) 3661–3679.

4. Solon Barocas and Helen Nissenbaum . 2014. Big data’s end run around anonymity and consent. Privacy, big data, and the public good: Frameworks for engagement 1 ( 2014 ), 44–75. Solon Barocas and Helen Nissenbaum. 2014. Big data’s end run around anonymity and consent. Privacy, big data, and the public good: Frameworks for engagement 1 (2014), 44–75.

5. The ethics of online research with unsuspecting users: From A/B testing to C/D experimentation

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