We are excited to share the latest research out of our lab. In a new paper published in Patterns, our team – including Yuhao Zhou, Wenhao Chen, our frequent collaborator Prof. María Óskarsdóttir, and Prof. Matt Davison – explores why women remain underrepresented in corporate boardrooms despite having the same qualifications as their male peers.

When people discuss the gender gap in leadership, the conversation usually turns to individual career choices, formal qualifications, or official diversity policies. However, we wanted to look deeper into the informal social processes that determine who gets noticed, trusted, and ultimately invited into these powerful roles.

To figure this out, we combined social network analysis with a causal learning framework and Long Short-Term Memory (LSTM) deep learning models. We analyzed a massive dataset tracking the career histories and network connections of more than 19,000 senior managers and board members across over 700 publicly traded Canadian firms over a 23-year period.

To avoid outlier or dissimilar paths, we used a matching methodology to pair female candidates with male counterparts who had highly similar demographic profiles and career trajectories. By keeping career paths constant, we were able to isolate the true impact that professional networks have on board appointments.

What we found reveals a clear, structural “glass ceiling” across networks. Even with identical experiences, women and men experience the benefits of networking very differently.

Here is what our models uncovered:

• The Network Premium: Women who successfully secure board seats tend to possess unusually broad and central professional connections. Essentially, women must clear a much higher informal threshold, building wider and more influential networks than men, to attain equivalent board roles.
• Different Paths to the Top: For men, educational and their current employment networks carry the greatest predictive weight for securing board appointments. Women, however, must rely on a much more balanced mix of connections, drawing upon informal social engagements alongside their formal professional networks to achieve the same outcomes.
• The Power of Female Mentorship: Our gender-specific analysis revealed that female-to-female professional ties are truly impactful. Existing female board members play a substantial and vital role in lifting other women into leadership positions. Targeted sponsorship and mentorship within underrepresented groups is key in career advancement.

These findings highlight that boardroom inequality is not just about hiring decisions; it is structurally embedded in everyday networking practices. Addressing these hidden barriers requires moving beyond individual qualifications to actively widen recruitment channels, reduce reliance on closed networks, and foster truly inclusive professional relationships.

Read the press releases from Western & Cell Press.

Our latest preprint is out! Check it out on ArXiV.

Imagine making important decisions—like adjusting credit limits or recommending treatments—where there are multiple options instead of only “yes or no”. In our latest research, we asked whether traditional methods of estimating causal effects are enough for these complex situations. But what exactly is a causal effect? Simply put, a causal effect measures how one action directly impacts an outcome—like how increasing a credit limit might influence a customer’s spending behavior. Typically, these causal estimates help us make the “best” decision, but we found they don’t always tell the whole story.

Our study dives into what’s called a “multitreatment scenario”, where the range of possible actions isn’t limited to just one choice. For example, adjusting a credit limit could involve not only deciding whether to change it, but also determining by how much. In our case, the outcome we aimed to optimize was continuous—for instance the expected profit from adjusting credit card limits, and also the outcome was observable both before and after treatment.

We explored a new method that combines causal effect estimation with additional criteria like uncertainty measures and a “prediction condition”.  To truly enhance decision quality, we went beyond the traditional methods and added a fresh perspective. We brought in a measure called Conditional Value at Risk (CVaR), which allowed us to understand potential worst-case scenarios, not just the average outcome. Think of CVaR as a safety net that ensures we don’t overlook possible downsides while aiming for the best result.

We also introduced a “predictive condition” to help ensure our recommendations would actually be beneficial. Essentially, the predictive condition means we only recommend an action if we can confidently predict that the outcome will improve compared to where it started. In the context of adjusting credit limits, this meant that any suggested change had to show a clear increase in expected profit compared to the one corresponded to the original limit. This safeguard adds another layer of confidence, making sure that each decision made was a true upgrade rather than just a guess.

When applied to financial decision-making—adjusting credit card limits for consumers—we found that this combined approach outperformed traditional models. It wasn’t just about predicting the treatment effect, but also understanding how confident we could be about that effect while balancing it against other possible outcomes.

What makes this important is its broader applicability. Whether it’s deciding a financial policy, optimizing healthcare treatment doses, or managing resources, this refined approach could change how we navigate complex decisions. By blending causal analysis with a richer understanding of uncertainty, we can make recommendations that are not just statistically sound, but also practically more effective and personalized.

Our study is a step towards smarter decision-making in areas where “one size fits all” just doesn’t cut it. This work helps ensure that recommendations, whether in finance, healthcare, or any other field, are optimized not just for an average effect but for the best possible outcome across a range of real-world scenarios.

You can also listen to a machine-generated podcast of the preprint below.