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1. IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data

   https://doi.org/10.24963/ijcai.2020/625  

   Guo, Ruocheng; Li, Jundong; Li, Yichuan; Candan, K. Selçuk; Raglin, Adrienne; Liu, Huan (July 2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   Networked observational data presents new opportunities for learning individual causal effects, which plays an indispensable role in decision making. Such data poses the challenge of confounding bias. Previous work presents two desiderata to handle confounding bias. On the treatment group level, we aim to balance the distributions of confounder representations. On the individual level, it is desirable to capture patterns of hidden confounders that predict treatment assignments. Existing methods show the potential of utilizing network information to handle confounding bias, but they only try to satisfy one of the two desiderata. This is because the two desiderata seem to contradict each other. When the two distributions of confounder representations are highly overlapped, then we confront the undiscriminating problem between the treated and the controlled. In this work, we formulate the two desiderata as a minimax game. We propose IGNITE that learns representations of confounders from networked observational data, which is trained by a minimax game to achieve the two desiderata. Experiments verify the efficacy of IGNITE on two datasets under various settings. 

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2. Fairness and robustness in anti-causal prediction

   Makar, Maggie; D'Amour, Alexander (February 2023, Transactions on machine learning research)

   Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem related, the connection between them is often unclear in practice. Here, we discuss these connections through a causal lens, focusing on anti-causal prediction tasks, where the input to a classifier (e.g., an image) is assumed to be generated as a function of the target label and the protected attribute. By taking this perspective, we draw explicit connections between a common fairness criterion - separation - and a common notion of robustness - risk invariance. These connections provide new motivation for applying the separation criterion in anticausal settings, and inform old discussions regarding fairness-performance tradeoffs. In addition, our findings suggest that robustness-motivated approaches can be used to enforce separation, and that they often work better in practice than methods designed to directly enforce separation. Using a medical dataset, we empirically validate our findings on the task of detecting pneumonia from X-rays, in a setting where differences in prevalence across sex groups motivates a fairness mitigation. Our findings highlight the importance of considering causal structure when choosing and enforcing fairness criteria. 

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3. Towards Testing Monotonicity of Distributions Over General Posets

   Alaikbarpour, A.; Gouleakis, T.; Peebles, J.; Rubinfeld, R.; Yodpinyanee, A (January 2019, Proceedings of the Thirty-Second Conference on Learning Theory (COLT 2019))

   In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is monotone if, for any pair of domain elements x and y such that x ⪯ y, p(x) ≤ p(y). To understand the sample complexity of this problem, we introduce a new property called bigness over a finite domain, where the distribution is T-big if the minimum probability for any domain element is at least T. We establish a lower bound of Ω(n/ log n) for testing bigness of distributions on domains of size n. We then build on these lower bounds to give Ω(n/ log n) lower bounds for testing monotonicity over a matching poset of size n and significantly improved lower bounds over the hypercube poset. We give sublinear sample complexity bounds for testing bigness and for testing monotonicity over the matching poset. We then give a number of tools for analyzing upper bounds on the sample complexity of the monotonicity testing problem. The previous lower bound for testing Monotonicity of 

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4. Robust Allocations with Diversity Constraints

   Shen, Zeyu; Gelauff, Lodewijk; Goel, Ashish; Korolova, Aleksandra; Munagala, Kamesh (December 2021, Advances in Neural Information Processing Systems)

   We consider the problem of allocating divisible items among multiple agents, and consider the setting where any agent is allowed to introduce {\emph diversity constraints} on the items they are allocated. We motivate this via settings where the items themselves correspond to user ad slots or task workers with attributes such as race and gender on which the principal seeks to achieve demographic parity. We consider the following question: When an agent expresses diversity constraints into an allocation rule, is the allocation of other agents hurt significantly? If this happens, the cost of introducing such constraints is disproportionately borne by agents who do not benefit from diversity. We codify this via two desiderata capturing {\em robustness}. These are {\emph no negative externality} -- other agents are not hurt -- and {\emph monotonicity} -- the agent enforcing the constraint does not see a large increase in value. We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is {\emph uniquely} positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules. We finally perform an empirical simulation on real-world data that models ad allocations to show that this gap between Nash Welfare and other rules persists in the wild. 

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5. Accessing the right to vote among system-impacted people

   https://doi.org/10.1177/14624745241230199  

   Sugie, Naomi_F; Sandoval, Juan_R; Kaiser, Daniela_E; Mosca, Delaney; Winnen, Kyle; Zhang, Emily_Rong; Zhang, Iris_H (September 2024, Punishment & Society)

   Recent efforts to dismantle felon disenfranchisement regimes have the potential to substantially expand electoral eligibility among people with criminal records; however, even among those with criminal legal histories who are eligible to vote, voting rates are often extremely low. Analyzing interview, focus group, and text message conversations among a multi-state sample around the November 2022 election, we identify and describe how administrative barriers to voting—including a lack of understanding about the voting process, confusion about legal eligibility, and perceived risks of rearrest of voting while ineligible—pose an access to justice issue among system-impacted people. These barriers are amplified by government mistrust, specifically the perception that barriers are intentionally constructed to suppress voting, and they are potentially mitigated by outreach by community organizations that are viewed as credible. The findings emphasize that legislative reforms repealing disenfranchisement laws must be accompanied by on-the-ground efforts to address administrative burdens to broaden access to the franchise. 

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