Valid Causal Inference with (Some) Invalid Instruments

• URL: http://arxiv.org/abs/2006.11386v1
• Date: Fri, 19 Jun 2020 21:09:26 GMT
• Title: Valid Causal Inference with (Some) Invalid Instruments
• Authors: Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown
• Abstract summary: We show how to perform consistent IV estimation despite violations of the exclusion assumption. We achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators.
• Score: 24.794879633855373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reliance on untestable "exclusion" assumptions that rule out any relationship between the instrument variable and the response that is not mediated by the treatment. In this paper, we show how to perform consistent IV estimation despite violations of the exclusion assumption. In particular, we show that when one has multiple candidate instruments, only a majority of these candidates---or, more generally, the modal candidate-response relationship---needs to be valid to estimate the causal effect. Our approach uses an estimate of the modal prediction from an ensemble of instrumental variable estimators. The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently. As such, it is compatible with recent machine-learning based estimators that allow for the estimation of conditional average treatment effects (CATE) on complex, high dimensional data. Experimentally, we achieve accurate estimates of conditional average treatment effects using an ensemble of deep network-based estimators, including on a challenging simulated Mendelian Randomization problem.

Related papers

• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
  Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs. We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint. We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
  arXiv  Detail & Related papers  (2024-06-05T13:36:38Z)
• Bounding Causal Effects with Leaky Instruments [6.2316012741781295]
  We propose a novel solution that provides $textitpartial$ identification in linear systems given a set of $textitleaky instruments$. We derive a convex optimization objective that provides provably sharp bounds on the average treatment effect under some common forms of information leakage.
  arXiv  Detail & Related papers  (2024-04-05T23:17:25Z)
• Mitigating LLM Hallucinations via Conformal Abstention [70.83870602967625]
  We develop a principled procedure for determining when a large language model should abstain from responding in a general domain. We leverage conformal prediction techniques to develop an abstention procedure that benefits from rigorous theoretical guarantees on the hallucination rate (error rate) Experimentally, our resulting conformal abstention method reliably bounds the hallucination rate on various closed-book, open-domain generative question answering datasets.
  arXiv  Detail & Related papers  (2024-04-04T11:32:03Z)
• Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional [3.0232957374216953]
  evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory. We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• Neighborhood Adaptive Estimators for Causal Inference under Network Interference [152.4519491244279]
  We consider the violation of the classical no-interference assumption, meaning that the treatment of one individuals might affect the outcomes of another. To make interference tractable, we consider a known network that describes how interference may travel. We study estimators for the average direct treatment effect on the treated in such a setting.
  arXiv  Detail & Related papers  (2022-12-07T14:53:47Z)
• Falsification before Extrapolation in Causal Effect Estimation [6.715453431174765]
  Causal effects in populations are often estimated using observational datasets. We propose a meta-algorithm that attempts to reject observational estimates that are biased.
  arXiv  Detail & Related papers  (2022-09-27T21:47:23Z)
• Robust and Agnostic Learning of Conditional Distributional Treatment Effects [62.44901952244514]
  The conditional average treatment effect (CATE) is the best point prediction of individual causal effects. In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE) We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems.
  arXiv  Detail & Related papers  (2022-05-23T17:40:31Z)
• Counterfactual Inference of the Mean Outcome under a Convergence of Average Logging Probability [5.596752018167751]
  This paper considers estimating the mean outcome of an action from samples obtained in adaptive experiments. In adaptive experiments, the probability of choosing an action is allowed to be sequentially updated based on past observations.
  arXiv  Detail & Related papers  (2021-02-17T19:05:53Z)
• Conformal Inference of Counterfactuals and Individual Treatment Effects [6.810856082577402]
  We propose a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects. Existing methods suffer from a significant coverage deficit even in simple models.
  arXiv  Detail & Related papers  (2020-06-11T01:03:32Z)
• Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model [71.9860741092209]
  Clinical researchers often select among and evaluate risk prediction models. Standard metrics calculated from retrospective data are only related to model utility under certain assumptions. When predictions are delivered repeatedly throughout time, the relationship between standard metrics and utility is further complicated.
  arXiv  Detail & Related papers  (2020-06-02T16:26:49Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.