Read the full article: Attribution Science: Distinguishing AI from Macroeconomic and Seasonal Layoffs in March 2026

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Excerpt:

Introduction In March 2026, dozens of companies announced large layoffs. To understand why jobs were lost, analysts must separate the effects of artificial intelligence (AI) from ordinary economic cycles, seasonal patterns, and policy changes. For example, a Los Angeles Times report found that tech firms cited AI in over 48,000 U.S. job cuts in 2025 (www.latimes.com), but observers warn that some companies may be using “AI” as an excuse while real causes include overexpansion or weak demand (www.latimes.com) (www.hrdive.com). Attribution science asks: were March 2026 layoffs primarily due to new AI tools, a drop in customer demand, normal seasonal turnover, or new regulations?

This article outlines a clear, step-by-step method to estimate the share of layoffs caused by AI versus other factors. First, we collect all layoff announcements (press releases, SEC filings, etc.) and use text classification to label the stated reasons (AI-related vs. demand-related vs. seasonal or regulatory). Second, we apply time-series decomposition to total job-loss data to remove normal seasonal cycles. Third, we construct synthetic controls – weighted “twin” scenarios drawn from similar firms or regions – to estimate what layoffs would have been without a specific AI shock. Finally, we validate our results by checking related indicators, such as dates when companies adopted major AI software and rising automation investment. Throughout, we document each step and test alternative assumptions. This transparent, data-driven workflow helps ensure that conclusions (and any policy advice) rest on solid evidence rather than anecdotes.

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