# The State of Applied Econometrics - Causality and Policy Evaluation

arXiv: https://arxiv.org/abs/1607.00699 · HTML: https://mlsanity.com/paper/1607.00699/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2016.

## Built on by (most cited first, 30 of 41)

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- [The Local Approach to Causal Inference under Network Interference](https://mlsanity.com/paper/2105.03810/) (2021)
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- [Counterfactuals for the Future](https://mlsanity.com/paper/2212.03974/) (2022)
- [Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters](https://mlsanity.com/paper/2408.09598/) (2024)
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- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators](https://mlsanity.com/paper/2306.07918/) (2023)
- [Spillover Detection for Donor Selection in Synthetic Control Models](https://mlsanity.com/paper/2406.11399/) (2024)
- [Causal Foundation Models with Continuous Treatments](https://mlsanity.com/paper/2605.15133/) (2026)
- [Differentially Private Synthetic Control](https://mlsanity.com/paper/2303.14084/) (2023)
- [Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms](https://mlsanity.com/paper/2502.06231/) (2025)
- [Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability](https://mlsanity.com/paper/2310.13240/) (2023)
- [What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness](https://mlsanity.com/paper/2506.04194/) (2025)
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- [STOAT: Spatial-Temporal Probabilistic Causal Inference Network](https://mlsanity.com/paper/2506.09544/) (2025)
- [Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies](https://mlsanity.com/paper/2603.00041/) (2026)
- [Invariant Causal Routing for Governing Social Norms in Online Market Economies](https://mlsanity.com/paper/2603.04534/) (2026)
- [Causal Foundation Models](https://mlsanity.com/paper/2609.03003/) (2026)
- [Learning Generalized Causal Structure in Time-series](https://mlsanity.com/paper/2112.03085/) (2021)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning](https://mlsanity.com/paper/2310.09545/) (2023)
- [Body Discovery of Embodied AI](https://mlsanity.com/paper/2503.19941/) (2025)
- [XplainAct: Visualization for Personalized Intervention Insights](https://mlsanity.com/paper/2507.14767/) (2025)

## Abstract (arXiv)

In this paper we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, where in each case we highlight recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses to make the identification strategies more credible. These include placebo analyses as well as sensitivity and robustness analyses. Third, we discuss recent advances in machine learning methods for causal effects. These advances include methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogeneous treatment effects.