In causal inference with observational studies, synthetic control (SC) has emerged as a prominent tool. SC has traditionally been applied to aggregate-level datasets, but more recent work has extended its use to individual-level data. As they contain a greater number of observed units, this shift introduces the curse of dimensionality to SC. To address this, we propose Cluster Synthetic Control (ClusterSC), based on the idea that groups of individuals may exist where behavior aligns internally but diverges between groups. ClusterSC incorporates a clustering step to select only the relevant donors for the target. We provide theoretical guarantees on the improvements induced by ClusterSC, supported by empirical demonstrations on synthetic and real-world datasets. The results indicate that ClusterSC consistently outperforms classical SC approaches.

Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool) to predict a counterfactual time series of interest (i.e., target unit) by analyzing the relationship between the target and the donor pool before the intervention. As synthetic control tools are increasingly applied to sensitive or proprietary data, formal privacy protections are often required. In this work, we suggest the first algorithms for differentially private synthetic control with explicit error bounds based on the analysis of the sensitivity of the synthetic control query. Our approach builds upon tools from non-private synthetic control and differentially private empirical risk minimization. We empirically evaluate the performance of our algorithms and show favorable results in a variety of parameter regimes.

The adoption of self-tracking services to improve health-related behavior is increasing. Although psychologists claim that thinking about the future has a motivational impact on current behavior and cognition, few studies have explored using future forecast in self-tracking services. In this paper, we explore how future forecast information can be used in the design of self-tracking services. We conducted a four-week study that qualitatively investigated 11 participants' perceptions of and practices with future forecast information. Participants used the FutureSelf app that we developed, which forecasts dieters' future weights and expected goal achievement rates based on their current behavior. The findings reveal that predicting future weight based on prior performance induced participants to imagine their future selves and reminded them of their ultimate goals. In fact, the predictions became the participants' primary source of motivation. We also suggest design implications for self-tracking services that forecast users' future statuses.

Recently, self-tracking devices such as wearable activity trackers have become more available to end users. While these emerging products are imbued with new characteristics in terms of human-computer interaction, it is still unclear how to describe and design for user experience in such devices. In this paper, we present a three-week field study, which aimed to unfold users' experience with wearable activity trackers. Drawing from Knapp's model of interaction stages in interpersonal relationship development, we propose three stages of relationship development between users and self-tracking devices: initiation & experimentation, intensifying & integration, and stagnation & termination. We highlight the challenges in each stage and design opportunities for future self-tracking devices.