text: 130,11,1178835353303695361,876274407995527169,David Madras,"New paper! ""Causal Modeling for Fairness in Dynamical Systems"" <LINK> w/ Elliot Creager, Toni Pitassi & Rich Zemel TLDR: Through a series of case studies, we show causal DAGs can act as a unifying framework for the literature on long-term unfairness. 1/n <LINK> Recently, many papers have presented models of unfairness in long-term systems. We show causal DAGs can be used as a unifying framework for this literature, + give several case studies. Eg. this graphical formulation of “Delayed Impact of Fair Machine Learning” (Liu et al.) 2/n <LINK> We give 3 advantages of causal DAGs in this domain. First is visualization: graphical models are a compact way to communicate models to non-technical stakeholders. Eg. this formulation of Hashimoto et al’s “Fairness w/o Demographics in Repeated Loss Minimization” 3/n <LINK> … or this graphical formulation of an intricate, two-stage labor market model in Hu et al’s “A Short-term Intervention for Long-term Fairness in the Labor Market”. 4/n <LINK> The second reason to use these graphical models is introspection: these formulations make clear many implicit causal assumptions, and present straightforward methods for discussion and modification. 5/n The third is evaluation: many long-term fairness papers must be evaluated using ""policy evaluation"" — we give examples in a case study. 6/n <LINK> Furthermore, causal models allow for counterfactual-based policy evaluation methods, which are more practical/robust. 7/n <LINK> In conclusion, we suggest a range of future questions at the intersection of various fields (e.g. RL, fairness, causal inference) which follow naturally from our formulation. n/n <LINK> PS: Since finishing this paper, I've become fairly sure there's a literature on structural econometrics which is very related to all of this. If anyone has points on good places to read up on that (#econtwitter? does that work?) that would be swell @jvmancuso This looks (tangentially) awesome! I definitely thought this field did not exist. @adversariel will enjoy I think @jvmancuso @adversariel Haha it's a small Twitter after all @jvmancuso Sounds cool! Fairness through awareness has a special place in my heart, I'll check this out",https://arxiv.org/abs/1909.09141,"In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamically changing environment to produce both immediate and long-term effects for individuals and demographic groups. We discuss causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in such dynamical systems. We show that this formulation affords several new directions of inquiry to the modeler, where causal assumptions can be expressed and manipulated. We emphasize the importance of computing interventional quantities in the dynamical fairness setting, and show how causal assumptions enable simulation (when environment dynamics are known) and off-policy estimation (when dynamics are unknown) of intervention on short- and long-term outcomes, at both the group and individual levels. ",Causal Modeling for Fairness in Dynamical Systems,12,"['New paper! ""Causal Modeling for Fairness in Dynamical Systems"" <LINK>  w/ Elliot Creager, Toni Pitassi &amp; Rich Zemel\n\nTLDR: Through a series of case studies, we show causal DAGs can act as a unifying framework for the literature on long-term unfairness. 1/n <LINK>', 'Recently, many papers have presented models of unfairness in long-term systems. We show causal DAGs can be used as a unifying framework for this literature, + give several case studies.\n\nEg. this graphical formulation of “Delayed Impact of Fair Machine Learning” (Liu et al.) 2/n https://t.co/ynUh7FHgkW', 'We give 3 advantages of causal DAGs in this domain. First is visualization: graphical models are a compact way to communicate models to non-technical stakeholders.\n\nEg. this formulation of Hashimoto et al’s “Fairness w/o Demographics in Repeated Loss Minimization” 3/n https://t.co/IVj6jwAWwR', '… or this graphical formulation of an intricate, two-stage labor market model in Hu et al’s “A Short-term Intervention for Long-term Fairness in the Labor Market”. 4/n https://t.co/RgtTjQmg3V', 'The second reason to use these graphical models is introspection: these formulations make clear many implicit causal assumptions, and present straightforward methods for discussion and modification. 5/n', 'The third is evaluation: many long-term fairness papers must be evaluated using ""policy evaluation"" — we give examples in a case study. 6/n https://t.co/sJrw4zYlvm', 'Furthermore, causal models allow for counterfactual-based policy evaluation methods, which are more practical/robust. 7/n https://t.co/pIeQ2HX6ad', 'In conclusion, we suggest a range of future questions at the intersection of various fields (e.g. RL, fairness, causal inference) which follow naturally from our formulation.  n/n https://t.co/FDJx7WRpP6', ""PS: Since finishing this paper, I've become fairly sure there's a literature on structural econometrics which is very related to all of this. If anyone has points on good places to read up on that (#econtwitter? does that work?) that would be swell"", '@jvmancuso This looks (tangentially) awesome!  I definitely thought this field did not exist. @adversariel will enjoy I think', ""@jvmancuso @adversariel Haha it's a small Twitter after all"", ""@jvmancuso Sounds cool! Fairness through awareness has a special place in my heart, I'll check this out""]",19,09,2200