In Disability Employment Services (DES), an emerging problem is to make job survival analysis stable in unseen environments without prior knowledge of these environments. Existing survival analysis methods cannot adequately solve this problem since they assume that distribution of unseen data is similar to that observed during training. However, this assumption can be violated in practice where unanticipated events such as COVID19 and inflation can change the work and life patterns of people with disability. Models trained before the COVID19 pandemic may make unreliable job survival predictions in COVID19 or inflation situations. It is also costly and time consuming to frequently re-train and deploy the models. This paper proposes a stable survival analysis method for the DES sector without requiring prior knowledge of deployment environments. Latent representations are learned to capture non-linear relationships between relevant features and job survival time. Two reweighting stages are developed to remove censoring and conditional spurious correlations between irrelevant features and the survival outcome. The case study of Australian workers with disability shows that our method can make stable risk predictions. It can also help workers with disability determine the most effective skills for improvement to increase their job survival time. Further evaluations with public datasets show the promising stable performance of our method in other applications.

In Disability Employment Service (DES), DES providers are confronted with "what-if" questions to assist workers with disability in deciding which skill should be improved to increase their job retention time. For instance, what would happen to the job retention time of a worker with disability if he improved his computer skill to an advanced level? This requires counterfactual inference to estimate the counterfactuals of the survival outcome, i.e., job retention time, under different skill improvement scenarios. While exiting survival analysis techniques are not designed for counterfactual problems, current counterfactual prediction methods are assumed to work with non-survival outcomes. In this paper, we propose the Counterfactual Survival Network (CSN), a representation learning based method for counterfactual survival prediction, where both confounding and censoring biases are removed based on latent representations. Since ground truth counterfactuals are unavailable, we develop a sample specific estimator to estimate counterfactuals for training samples. These estimated counterfactual outcomes are used as pseudo ground truth to train the counterfactual prediction model. We demonstrate the benefits of our method in decision support tasks with the case study of Australian workers and three public datasets. Results show that CSN can help Australian workers with disability increase their job retention time. Our method also shows its promising performance in other applications.

In Disability Employment Services (DES), an emerging problem is recommending to disabled workers the right skill to upgrade and the right upgrade level to achieve a maximum increase in their job retention time. This problem involves causal reasoning to estimate the individual causal effect (ICE) on the survival outcome, i.e., job retention time, to determine the most effective intervention for a worker. Existing methods are not suitable to solve our problem. They are mostly developed for non-causal or non-survival challenges, while methods for causal survival analysis are under-explored. This paper proposes a representation learning method for recommending personalized interventions that can generate a maximum increase in job retention time for workers with disability. In our method, observed covariates are disentangled into latent variables based on which confounding and censoring biases are eliminated, and the ICE prediction model is built. Since true ICE values are not directly measurable in observational data, a reverse engineering technique is developed to estimate ICE for training samples. These estimated ICE values are then used as the pseudo ground truth to train the prediction model. Experiments with a case study of Australian workers with disability show that by adopting personalized interventions recommended by our method, disabled workers can increase their job retention time by up to 2.8 months. Additional evaluations with public datasets also show the technical strengths of our method in other applications.

In Disability Employment Services (DES), a growing problem is recommending to disabled job seekers which skill should be upgraded and the best level for upgrading this skill to increase their employment potential most. This problem involves counterfactual reasoning to infer causal effect of factors on employment status to recommend the most effective intervention. Related methods cannot solve our problem adequately since they are developed for non-counterfactual challenges, for binary causal factors, or for randomized trials. In this paper, we present a causality-based method to tackle the problem. The method includes two stages where causal factors of employment status are first detected from data. We then combine a counterfactual reasoning framework with a machine learning approach to build an interpretable model for generating personalized recommendations. Experiments on both synthetic datasets and a real case study from a DES provider show consistent promising performance of improving employability of disabled job seekers. Results from the case study disclose effective factors and their best levels for intervention to increase employability. The most effective intervention varies among job seekers. Our model can separate job seekers by degree of employability increase. This is helpful for DES providers to allocate resources for employment assistance. Moreover, causal interpretability makes our recommendations actionable in DES business practice.

In the disability employment service, an emerging challenge is to recommend the skills whose improvement will increase the employment perspective most. The process of a skill being improved is called an intervention and different skills are called factors. The problem involves recommendation for outcome improvement, which requires estimating the improvement in the employment perspective, i.e., the outcome, driven by interventions on recommended factors. Currently, most recommendation systems deployed for the employment service rely on traditional recommendation models where the desired outcome instead of the degree of outcome improvement is the main goal for optimization. In this paper, we present a causality-based approach for recommending factors for intervention to achieve the largest improvement in the employment potential of disabled job seekers. It involves inferring the causal effect of interventions on the employment outcome to make recommendations for individuals. The causal interpretation of our model can justify given recommendations. We conduct a case study with our industry partner in the disability employment service. Results show that the recommended interventions could improve the employability of disabled job seekers. Experiments are also carried out with datasets in other domains to demonstrate the promise of our approach in different applications.