56 research outputs found
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Recent advances in probabilistic generative modeling have motivated learning
Structural Causal Models (SCM) from observational datasets using deep
conditional generative models, also known as Deep Structural Causal Models
(DSCM). If successful, DSCMs can be utilized for causal estimation tasks, e.g.,
for answering counterfactual queries. In this work, we warn practitioners about
non-identifiability of counterfactual inference from observational data, even
in the absence of unobserved confounding and assuming known causal structure.
We prove counterfactual identifiability of monotonic generation mechanisms with
single dimensional exogenous variables. For general generation mechanisms with
multi-dimensional exogenous variables, we provide an impossibility result for
counterfactual identifiability, motivating the need for parametric assumptions.
As a practical approach, we propose a method for estimating worst-case errors
of learned DSCMs' counterfactual predictions. The size of this error can be an
essential metric for deciding whether or not DSCMs are a viable approach for
counterfactual inference in a specific problem setting. In evaluation, our
method confirms negligible counterfactual errors for an identifiable SCM from
prior work, and also provides informative error bounds on counterfactual errors
for a non-identifiable synthetic SCM
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Recent empirical studies on domain generalization (DG) have shown that DG
algorithms that perform well on some distribution shifts fail on others, and no
state-of-the-art DG algorithm performs consistently well on all shifts.
Moreover, real-world data often has multiple distribution shifts over different
attributes; hence we introduce multi-attribute distribution shift datasets and
find that the accuracy of existing DG algorithms falls even further. To explain
these results, we provide a formal characterization of generalization under
multi-attribute shifts using a canonical causal graph. Based on the
relationship between spurious attributes and the classification label, we
obtain realizations of the canonical causal graph that characterize common
distribution shifts and show that each shift entails different independence
constraints over observed variables. As a result, we prove that any algorithm
based on a single, fixed constraint cannot work well across all shifts,
providing theoretical evidence for mixed empirical results on DG algorithms.
Based on this insight, we develop Causally Adaptive Constraint Minimization
(CACM), an algorithm that uses knowledge about the data-generating process to
adaptively identify and apply the correct independence constraints for
regularization. Results on fully synthetic, MNIST, small NORB, and Waterbirds
datasets, covering binary and multi-valued attributes and labels, show that
adaptive dataset-dependent constraints lead to the highest accuracy on unseen
domains whereas incorrect constraints fail to do so. Our results demonstrate
the importance of modeling the causal relationships inherent in the
data-generating process
Conjunction of factors triggering waves of seasonal influenza
Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population’s socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus’ antigenic drift over time; (4) the host population’€™s land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies
Autonomous Recovery in Componentized Internet Applications
In this paper we show how to reduce downtime of J2EE applications by rapidly and automatically recovering from transient and intermittent software failures, without requiring application modifications. Our prototype combines three application-agnostic techniques: macroanalysis for fault detection and localization, microrebooting for rapid recovery, and external management of recovery actions. The individual techniques are autonomous and work across a wide range of componentized Internet applications, making them well-suited to the rapidly changing software of Internet services. The proposed framework has been integrated with JBoss, an open-source J2EE application server. Our prototype provides an execution platform that can automatically recover J2EE applications within seconds of the manifestation of a fault. Our system can provide a subset of a system's active end users with the illusion of continuous uptime, in spite of failures occurring behind the scenes, even when there is no functional redundancy in the system
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
Although systematic biases in decision-making are widely documented, the ways
in which they emerge from different sources is less understood. We present a
controlled experimental platform to study gender bias in hiring by decoupling
the effect of world distribution (the gender breakdown of candidates in a
specific profession) from bias in human decision-making. We explore the
effectiveness of \textit{representation criteria}, fixed proportional display
of candidates, as an intervention strategy for mitigation of gender bias by
conducting experiments measuring human decision-makers' rankings for who they
would recommend as potential hires. Experiments across professions with varying
gender proportions show that balancing gender representation in candidate
slates can correct biases for some professions where the world distribution is
skewed, although doing so has no impact on other professions where human
persistent preferences are at play. We show that the gender of the
decision-maker, complexity of the decision-making task and over- and
under-representation of genders in the candidate slate can all impact the final
decision. By decoupling sources of bias, we can better isolate strategies for
bias mitigation in human-in-the-loop systems.Comment: This paper has been accepted for publication at HCOMP 201
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